nnU-Net_Workshop.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"9GejzW3QmYc0"},"source":["# nnU-Net V2 Workshop \n","The nnU-Net is a toolkit to segment imaging data and was specifically designed for biomedical data.\n","\n","It was designed by **Fabian Isensee** while pursuing his PhD at the [Medical Image Computing Division of the German Cancer Research Institute](https://www.dkfz.de/en/mic/index.php).\n","\n","More information about nnU-Net, as well as the installataion guide on your own workstation can be found on:\n","- Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J. & Maier-Hein, K. H. [nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation](http://www.nature.com/articles/s41592-020-01008-z). Nat Methods 18, 203–211 (2021).\n","- [nnU-Net GitHub Page](https://github.com/MIC-DKFZ/nnUNet)\n","\n","\n","## What is this Workshop all about?\n","\n","The goal of this workshop is to allow end-users to understand in how to use nnU-Net version 2 for their own applications.\n","\n","It includes initial setup and how to use nnU-Net in the Google Colab environment. \n","Some steps are only needed for GoogleColab and will be explained, while others are generally applicable. \n","\n","Further it is advised to visualize the data with segmentations using the [MITK-Workbench](https://www.mitk.org/wiki/The_Medical_Imaging_Interaction_Toolkit_(MITK)), which is also developed in the Medical Image Computing Division of the German Cancer Research Institute - at least this is what we advocate.\n","\n","- [MITK Workbench Download Link](https://www.mitk.org/wiki/Downloads)\n","\n","### Requirements for Google Colab \n","1. Google Account\n","   1. At least 4GB of free disk space\n","2. Visualization tool for volumetric data, we advise to use MITK.\n","\n","### Some notes about the Workshop\n","\n","This workshop was created by **Carsten Lüth**, a PhD student in the [Interactive Machine Learning Research Group](https://www.dkfz.de/en/interaktives-maschinelles-lernen/index.php) headed by Dr. Paul F. Jäger.\n","\n","If you happen to find this workshop in any way useful, please share this version of it:\n","- [https://github.com/IML-DKFZ/nnunet-workshop](https://github.com/IML-DKFZ/nnunet-workshop)\n","\n","## Further information\n","\n","Further Information about nnU-Net is presented in the following lectures:\n","- [Lecture by Paul F. Jäger](https://www.youtube.com/watch?v=3po8qVzz5Tc&t=2196s)\n","- [Lecture by Fabian Isensee](https://www.youtube.com/watch?v=C6tpnJRpt90)\n","\n","### MITK with nnU-Net\n","MITK has a new experimental feature which uses pretrained nnU-Net models as a segmentation tool.\n","This feature has been added since its release of version 2022.04.\n","For further information about this we refer to the [MITK Documentation](https://docs.mitk.org/2022.04/org_mitk_views_segmentation.html#org_mitk_views_segmentationnnUNetTool).\n","\n","\n","**Notes**:\n","- quite some time was spent to optimize the way of downloading and unpacking the data. I did not get to run it at all when automated... If you have any idea how to do this better (and show that it works I would greatly appreciate this!)"]},{"cell_type":"markdown","metadata":{"id":"THifmOYu9Cip"},"source":["# 1. Import Packages for Google Colab"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k-hj38QV_raZ"},"outputs":[],"source":["# Import basic packages for later use\n","import os\n","import shutil\n","from collections import OrderedDict\n","\n","import json\n","import matplotlib.pyplot as plt\n","import nibabel as nib\n","\n","import numpy as np\n","import torch"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ld3_-quILggy"},"outputs":[],"source":["# check whether GPU accelerated computing is available\n","assert torch.cuda.is_available() # if there is an error here, enable GPU in the Runtime"]},{"cell_type":"markdown","metadata":{"id":"aNfZ1NHt9WeI"},"source":["# 2. Installing nnU-Net"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":22403,"status":"ok","timestamp":1643208064772,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"K--4dWRI_xGW","outputId":"42890394-93a6-463f-8ad1-76c35c49524a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting nnunet\n","  Downloading nnunet-1.7.0.tar.gz (251 kB)\n","\u001b[?25l\r\u001b[K     |█▎                              | 10 kB 19.0 MB/s eta 0:00:01\r\u001b[K     |██▋                             | 20 kB 23.3 MB/s eta 0:00:01\r\u001b[K     |████                            | 30 kB 26.6 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 40 kB 19.9 MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 51 kB 16.0 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 61 kB 14.2 MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 71 kB 11.3 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 81 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 92 kB 13.4 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 102 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 112 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 122 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 133 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 143 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |███████████████████▌            | 153 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 163 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 174 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 184 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 194 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 204 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 215 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 225 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 235 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 245 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 251 kB 12.6 MB/s \n","\u001b[?25hRequirement already satisfied: torch>=1.6.0a in /usr/local/lib/python3.7/dist-packages (from nnunet) (1.10.0+cu111)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nnunet) (4.62.3)\n","Collecting dicom2nifti\n","  Downloading dicom2nifti-2.3.0.tar.gz (33 kB)\n","Requirement already satisfied: scikit-image>=0.14 in /usr/local/lib/python3.7/dist-packages (from nnunet) (0.18.3)\n","Collecting medpy\n","  Downloading MedPy-0.4.0.tar.gz (151 kB)\n","\u001b[K     |████████████████████████████████| 151 kB 45.1 MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from nnunet) (1.4.1)\n","Collecting batchgenerators>=0.23\n","  Downloading batchgenerators-0.23.tar.gz (57 kB)\n","\u001b[K     |████████████████████████████████| 57 kB 5.2 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nnunet) (1.19.5)\n","Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from nnunet) (0.0)\n","Requirement already satisfied: SimpleITK in /usr/local/lib/python3.7/dist-packages (from nnunet) (2.0.2)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from nnunet) (1.1.5)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from nnunet) (2.23.0)\n","Requirement already satisfied: nibabel in /usr/local/lib/python3.7/dist-packages (from nnunet) (3.0.2)\n","Requirement already satisfied: tifffile in /usr/local/lib/python3.7/dist-packages (from nnunet) (2021.11.2)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from batchgenerators>=0.23->nnunet) (7.1.2)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from batchgenerators>=0.23->nnunet) (1.0.2)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from batchgenerators>=0.23->nnunet) (0.16.0)\n","Collecting unittest2\n","  Downloading unittest2-1.1.0-py2.py3-none-any.whl (96 kB)\n","\u001b[K     |████████████████████████████████| 96 kB 6.0 MB/s \n","\u001b[?25hRequirement already satisfied: threadpoolctl in /usr/local/lib/python3.7/dist-packages (from batchgenerators>=0.23->nnunet) (3.0.0)\n","Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.14->nnunet) (1.2.0)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.14->nnunet) (3.2.2)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.14->nnunet) (2.4.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.14->nnunet) (2.6.3)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.14->nnunet) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.14->nnunet) (0.11.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.14->nnunet) (2.8.2)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.14->nnunet) (3.0.6)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.14->nnunet) (1.15.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0a->nnunet) (3.10.0.2)\n","Collecting pydicom>=1.3.0\n","  Downloading pydicom-2.2.2-py3-none-any.whl (2.0 MB)\n","\u001b[K     |████████████████████████████████| 2.0 MB 44.8 MB/s \n","\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->nnunet) (2018.9)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->nnunet) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->nnunet) (2021.10.8)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->nnunet) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->nnunet) (3.0.4)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->batchgenerators>=0.23->nnunet) (1.1.0)\n","Collecting argparse\n","  Downloading argparse-1.4.0-py2.py3-none-any.whl (23 kB)\n","Collecting traceback2\n","  Downloading traceback2-1.4.0-py2.py3-none-any.whl (16 kB)\n","Collecting linecache2\n","  Downloading linecache2-1.0.0-py2.py3-none-any.whl (12 kB)\n","Building wheels for collected packages: nnunet, batchgenerators, dicom2nifti, medpy\n","  Building wheel for nnunet (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for nnunet: filename=nnunet-1.7.0-py3-none-any.whl size=487821 sha256=13eaebaba6a6193002385a2d21b302108b66247448de3802584ed33efd4089aa\n","  Stored in directory: /root/.cache/pip/wheels/66/f6/19/c51389976a9ae30212178d1c41497893504b900f043fe1c665\n","  Building wheel for batchgenerators (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for batchgenerators: filename=batchgenerators-0.23-py3-none-any.whl size=84779 sha256=d5d6bfedfc9240e0afa65847249ada5cfd668f0317fc89fd220109696d6d25ea\n","  Stored in directory: /root/.cache/pip/wheels/81/3d/a2/9a602ce56458c684d2962ec89eddd6bc06b00687735e2dd11a\n","  Building wheel for dicom2nifti (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for dicom2nifti: filename=dicom2nifti-2.3.0-py3-none-any.whl size=42932 sha256=becb646cdb13e8c24abfc4f47d49effc82562dfe92922bb2e94c7a8a2d0d9364\n","  Stored in directory: /root/.cache/pip/wheels/bf/ff/aa/f5b52fd2e9f9355a28a6f6f6994d721a13209387528b146db5\n","  Building wheel for medpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for medpy: filename=MedPy-0.4.0-cp37-cp37m-linux_x86_64.whl size=754477 sha256=156ef81fd72875a52457ebbf17255716db1effd199268e56ebd3c9d28b02983a\n","  Stored in directory: /root/.cache/pip/wheels/b0/57/3a/da1183f22a6afb42e11138daa6a759de233fd977a984333602\n","Successfully built nnunet batchgenerators dicom2nifti medpy\n","Installing collected packages: linecache2, traceback2, argparse, unittest2, pydicom, medpy, dicom2nifti, batchgenerators, nnunet\n","Successfully installed argparse-1.4.0 batchgenerators-0.23 dicom2nifti-2.3.0 linecache2-1.0.0 medpy-0.4.0 nnunet-1.7.0 pydicom-2.2.2 traceback2-1.4.0 unittest2-1.1.0\n"]},{"data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["argparse"]}}},"metadata":{},"output_type":"display_data"}],"source":["# install nnunet - yes it is that easy\n","!pip install nnunetv2\n"]},{"cell_type":"markdown","metadata":{"id":"BtDKBF_4SJV8"},"source":["**Note**: you do not have to restart the Runtime even when the following error appears, this is simply due to a reinstallation of a package - so no worries. \n","\n","\n","```\n","WARNING: The following packages were previously imported in this runtime:\n","\n","[argparse]\n","\n","You must restart the runtime in order to use newly installed versions.\n","```"]},{"cell_type":"markdown","metadata":{"id":"oE-RujwI9efB"},"source":["# 3. Verifying installation of nn-Unet"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":477,"status":"ok","timestamp":1643208071545,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"7aFVfOoP_2XS","outputId":"570e2e8f-2869-4014-c3af-3c4e664a0594"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n"]}],"source":["# check if nnunet can be imported\n","import nnunetv2\n"]},{"cell_type":"markdown","metadata":{"id":"eDq5g_uk9pBU"},"source":["You should read the following if the installation was successful:\n","\n","\n","```\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","```\n","\n"]},{"cell_type":"markdown","metadata":{"id":"t9U4Uy_jSv7Z"},"source":["## 3.1 Installation and initialization of Weights&Biases (non-mandatory)\n","This is a package which allows you to easily visualize metrics during your training.\n","\n","We will use this here to get access to System Information (GPU utilization etc.)when running on Google Colab - however it is also useful to use at a local workstation.\n","\n","To use Weights&Biases you will need an account.\n","This can be created at https://wandb.ai\n","\n","During the initialization with ```wandb.init()``` it will ask you for an API key, which you can obtain from:\n","https://wandb.ai/settings \n","under API keys"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9552,"status":"ok","timestamp":1643208093086,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"04pXUQbq7_Sy","outputId":"f0e8cdda-ee22-45f2-aeda-53139b5131b5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting wandb\n","  Downloading wandb-0.12.9-py2.py3-none-any.whl (1.7 MB)\n","\u001b[?25l\r\u001b[K     |▏                               | 10 kB 20.1 MB/s eta 0:00:01\r\u001b[K     |▍                               | 20 kB 26.7 MB/s eta 0:00:01\r\u001b[K     |▋                               | 30 kB 23.9 MB/s eta 0:00:01\r\u001b[K     |▊                               | 40 kB 18.1 MB/s eta 0:00:01\r\u001b[K     |█                               | 51 kB 15.0 MB/s eta 0:00:01\r\u001b[K     |█▏                              | 61 kB 13.7 MB/s eta 0:00:01\r\u001b[K     |█▍                              | 71 kB 12.9 MB/s eta 0:00:01\r\u001b[K     |█▌                              | 81 kB 14.2 MB/s eta 0:00:01\r\u001b[K     |█▊                              | 92 kB 14.3 MB/s eta 0:00:01\r\u001b[K     |██                              | 102 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██                              | 112 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██▎                             | 122 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██▌                             | 133 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██▊                             | 143 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██▉                             | 153 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███                             | 163 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███▎                            | 174 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███▍                            | 184 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███▋                            | 194 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███▉                            | 204 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████                            | 215 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 225 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████▍                           | 235 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████▋                           | 245 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████▉                           | 256 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████                           | 266 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 276 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████▍                          | 286 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 296 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 307 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████                          | 317 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████▏                         | 327 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 337 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 348 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████▊                         | 358 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████▉                         | 368 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████                         | 378 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 389 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████▌                        | 399 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 409 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 419 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████                        | 430 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 440 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 450 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████▋                       | 460 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 471 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 481 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 491 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 501 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████▋                      | 512 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 522 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████                      | 532 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 542 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████▎                     | 552 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 563 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 573 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 583 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 593 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████▎                    | 604 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 614 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 624 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████▉                    | 634 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 645 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████▎                   | 655 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 665 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 675 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 686 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 696 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 706 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 716 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 727 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▊                  | 737 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 747 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▏                 | 757 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 768 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▌                 | 778 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 788 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 798 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 808 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 819 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▌                | 829 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 839 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 849 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 860 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 870 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 880 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 890 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▉               | 901 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 911 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▏              | 921 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 931 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 942 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 952 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 962 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 972 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 983 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 993 kB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 1.0 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 1.0 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 1.0 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▎            | 1.0 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▌            | 1.0 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▉            | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▌           | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 1.1 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▋          | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▉          | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▍         | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▎        | 1.2 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▊        | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▉       | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 1.3 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▉      | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 1.4 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▊    | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 1.5 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▉  | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▏ | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▉ | 1.6 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 1.7 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 1.7 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 1.7 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 1.7 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 1.7 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 1.7 MB 12.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 1.7 MB 12.3 MB/s \n","\u001b[?25hRequirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n","Collecting subprocess32>=3.5.3\n","  Downloading subprocess32-3.5.4.tar.gz (97 kB)\n","\u001b[K     |████████████████████████████████| 97 kB 7.9 MB/s \n","\u001b[?25hRequirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n","Collecting docker-pycreds>=0.4.0\n","  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n","Collecting configparser>=3.8.1\n","  Downloading configparser-5.2.0-py3-none-any.whl (19 kB)\n","Collecting shortuuid>=0.5.0\n","  Downloading shortuuid-1.0.8-py3-none-any.whl (9.5 kB)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.17.3)\n","Collecting yaspin>=1.0.0\n","  Downloading yaspin-2.1.0-py3-none-any.whl (18 kB)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Collecting GitPython>=1.0.0\n","  Downloading GitPython-3.1.26-py3-none-any.whl (180 kB)\n","\u001b[K     |████████████████████████████████| 180 kB 26.8 MB/s \n","\u001b[?25hRequirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n","Collecting pathtools\n","  Downloading pathtools-0.1.2.tar.gz (11 kB)\n","Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.2)\n","Collecting sentry-sdk>=1.0.0\n","  Downloading sentry_sdk-1.5.4-py2.py3-none-any.whl (143 kB)\n","\u001b[K     |████████████████████████████████| 143 kB 48.2 MB/s \n","\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (3.10.0.2)\n","Collecting gitdb<5,>=4.0.1\n","  Downloading gitdb-4.0.9-py3-none-any.whl (63 kB)\n","\u001b[K     |████████████████████████████████| 63 kB 1.7 MB/s \n","\u001b[?25hCollecting smmap<6,>=3.0.1\n","  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2021.10.8)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: termcolor<2.0.0,>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from yaspin>=1.0.0->wandb) (1.1.0)\n","Building wheels for collected packages: subprocess32, pathtools\n","  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for subprocess32: filename=subprocess32-3.5.4-py3-none-any.whl size=6502 sha256=eaaf0543df0294cf3cb2583c2d14179e8dbc32e10d1d05b3df4763dde834f8b6\n","  Stored in directory: /root/.cache/pip/wheels/50/ca/fa/8fca8d246e64f19488d07567547ddec8eb084e8c0d7a59226a\n","  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=91c1dab069184554e80786c52b386f2b89145dfb96f327d8c5372f07bd38d789\n","  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n","Successfully built subprocess32 pathtools\n","Installing collected packages: smmap, gitdb, yaspin, subprocess32, shortuuid, sentry-sdk, pathtools, GitPython, docker-pycreds, configparser, wandb\n","Successfully installed GitPython-3.1.26 configparser-5.2.0 docker-pycreds-0.4.0 gitdb-4.0.9 pathtools-0.1.2 sentry-sdk-1.5.4 shortuuid-1.0.8 smmap-5.0.0 subprocess32-3.5.4 wandb-0.12.9 yaspin-2.1.0\n"]}],"source":["!pip install wandb"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":522},"executionInfo":{"elapsed":34256,"status":"ok","timestamp":1643208132962,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"LcEDj7vrYmkK","outputId":"e721c305-97b0-4c76-f162-abe2e3b5f7ec"},"outputs":[{"data":{"application/javascript":"\n        window._wandbApiKey = new Promise((resolve, reject) => {\n            function loadScript(url) {\n            return new Promise(function(resolve, reject) {\n                let newScript = document.createElement(\"script\");\n                newScript.onerror = reject;\n                newScript.onload = resolve;\n                document.body.appendChild(newScript);\n                newScript.src = url;\n            });\n            }\n            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n            const iframe = document.createElement('iframe')\n            iframe.style.cssText = \"width:0;height:0;border:none\"\n            document.body.appendChild(iframe)\n            const handshake = new Postmate({\n                container: iframe,\n                url: 'https://wandb.ai/authorize'\n            });\n            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n            handshake.then(function(child) {\n                child.on('authorize', data => {\n                    clearTimeout(timeout)\n                    resolve(data)\n                });\n            });\n            })\n        });\n    ","text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n"]},{"name":"stdout","output_type":"stream","text":["wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit: ··········\n"]},{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"data":{"text/html":["\n","                    Syncing run <strong><a href=\"https://wandb.ai/ctlueth/nnU-Net_Workshop/runs/1gnefqql\" target=\"_blank\">firm-fire-1</a></strong> to <a href=\"https://wandb.ai/ctlueth/nnU-Net_Workshop\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n","\n","                "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/ctlueth/nnU-Net_Workshop/runs/1gnefqql?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"],"text/plain":["<wandb.sdk.wandb_run.Run at 0x7fe40df533d0>"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import wandb\n","wandb.init(project=\"nnU-Net_Workshop\")"]},{"cell_type":"markdown","metadata":{"id":"MHOVufx9BOT6"},"source":["### 3.2 Installation of GDOWN (only for in-person workshops)\n","GDown allows to download files from GoogleDrive with Python.\n","Therefore it is used for in-person workshops."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3498,"status":"ok","timestamp":1643208184049,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"deZRQMrRxfWX","outputId":"af59c39f-9e02-4337-b1e1-1d685c77081c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (3.6.4)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from gdown) (2.23.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown) (4.62.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (2021.10.8)\n"]}],"source":["# install gdown to download files from GoogleDrive\n","!pip install gdown"]},{"cell_type":"markdown","metadata":{"id":"tgssjiuNVvq5"},"source":["# 4. Connect Google Colab with GoogleDrive\n","This is heavily encouraged when using Google Colab, otherwise loading, accessing and saving of data checkpoints etc. is just much harder (if not impossible).\n","\n","**Note**:\n","When ```drive.mount()``` is executed, there will appear a popup window, which will ask you which Google Account should be connected and then for permission regarding Colab accessing your GoogleDrive.\n","\n","There can be some problems at this step, for me the solution was to Download the GoogleDrive Application.\n","You can download it from: https://www.google.com/drive/download/\n","\n","\n","If there are any problems at this stage, feel free to contact me (during the workshop)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23111,"status":"ok","timestamp":1643208211084,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"_WLi-mVRjbfb","outputId":"3297794c-79fb-46a5-b976-8dbedd47942d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["# for colab users only - mounting the drive\n","\n","from google.colab import drive\n","drive.mount('/content/drive',force_remount = True)\n","\n","drive_dir = \"/content/drive/My Drive\"\n","mount_dir = os.path.join(drive_dir, \"Colab Notebooks\")\n","base_dir = os.getcwd()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AheBsrsiYmkY"},"outputs":[],"source":["assert os.path.exists(drive_dir) # if this fails, something went wrong with mounting GoogleDrive\n","if os.path.exists(mount_dir) is False:\n","    os.makedirs(mount_dir)"]},{"cell_type":"markdown","metadata":{"id":"d-CV4Jc4W77P"},"source":["# 5. Setting up nnU-Nets folder structure and environment variables\n","nnUnet expects a certain folder structure and environment variables. \n","\n","Roughly they tell nnUnet:\n","1. Where to look for stuff\n","2. Where to put stuff\n","\n","For more information about this please check: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/setting_up_paths.md"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T9ifLrYhjfAT"},"outputs":[],"source":["def make_if_dont_exist(folder_path,overwrite=False):\n","    \"\"\"\n","    creates a folder if it does not exists\n","    input: \n","    folder_path : relative path of the folder which needs to be created\n","    over_write :(default: False) if True overwrite the existing folder \n","    \"\"\"\n","    if os.path.exists(folder_path):\n","        \n","        if not overwrite:\n","            print(f\"{folder_path} exists.\")\n","        else:\n","            print(f\"{folder_path} overwritten\")\n","            shutil.rmtree(folder_path)\n","            os.makedirs(folder_path)\n","\n","    else:\n","      os.makedirs(folder_path)\n","      print(f\"{folder_path} created!\")"]},{"cell_type":"markdown","metadata":{"id":"JAvjVPF0_7t3"},"source":["## 5.1 Set environment Variables and creating folders"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1643208223481,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"3rlqq-V-CWh8","outputId":"f9978966-693e-4591-fe5c-48fe9695e9bb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Current Working Directory /content\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed exists.\n","/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder exists.\n","/content/drive/My Drive/Colab Notebooks/RawData exists.\n","If No Error Occured Continue Forward. =)\n"]}],"source":["# Maybe move path of preprocessed data directly on content - this may be signifcantely faster!\n","print(\"Current Working Directory {}\".format(os.getcwd()))\n","path_dict = {\n","    \"nnUNet_raw\" : os.path.join(mount_dir, \"nnUNet_raw\"), \n","    \"nnUNet_preprocessed\" : os.path.join(mount_dir, \"nnUNet_preprocessed\"), # 1 experiment: 1 epoch took 112s\n","    \"nnUNet_results\" : os.path.join(mount_dir, \"nnUNet_results\"),\n","    \"RAW_DATA_PATH\" : os.path.join(mount_dir, \"RawData\"), # This is used here only for convenience (not necessary for nnU-Net)!\n","}\n","\n","# Write paths to environment variables\n","for env_var, path in path_dict.items():\n","  os.environ[env_var] = path \n","\n","# Check whether all environment variables are set correct!\n","for env_var, path in path_dict.items():\n","  if os.getenv(env_var) != path:\n","    print(\"Error:\")\n","    print(\"Environment Variable {} is not set correctly!\".format(env_var))\n","    print(\"Should be {}\".format(path))\n","    print(\"Variable is {}\".format(os.getenv(env_var)))\n","  make_if_dont_exist(path, overwrite=False)\n","\n","print(\"If No Error Occured Continue Forward. =)\")"]},{"cell_type":"markdown","metadata":{"id":"oLG69VKoX-BA"},"source":["# 6. Using nnU-Net on Medical Decathlon datasets\n","To get us started and also verify that nnU-Net works properly on our system, we will now run a training with nnU-Net and then make some predictions.\n","\n","This will be done on parts of the Medical Decathlon Dataset, because everything is already nicely set up for these datasets.\n","\n","Also for these two datasets there exist pre-written scripts, automatically unpacking and preparing the data for nnU-Net to use.\n","\n","For this we will:\n","- Train on the hippocampus dataset\n","- Run Inference on the prostate dataset\n","\n","Bottom Line:\n","If something goes wrong here, it is worth to check whether anything went wrong before these steps! "]},{"cell_type":"markdown","metadata":{"id":"iBxlyQ3rY7X_"},"source":["## 6.1 Downloading the Data\n","Download the data, unpack it and put it in the proper folder.\n","Generally, you will then have to download and move the files to the correct folder yourself.\n","\n","Link to official [GoogleDrive](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2) for the download.\n","\n","If you are doing this by hand - skip the two next cells!"]},{"cell_type":"markdown","metadata":{},"source":["### 6.1.1 Downloading the Data for in-person workshops\n","For in person workshops, this will be automated and I host the data from GoogleDrive.\n","This is due to time constraints of this workshop and I will take the links down later!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VHp6RxzNPjka"},"outputs":[],"source":["# only for in person workshops\n","\n","# Download the data with gdown\n","\n","os.chdir(path_dict[\"RAW_DATA_PATH\"])\n","# Download the Hippocampus Dataset\n","!gdown 'https://drive.google.com/uc?export=download&id=1L-22VV6J8O6afTSOQuQKFiH-tblxb_TW'\n","\n","# Download the Prostate Dataset\n","!gdown  'https://drive.google.com/uc?export=download&id=1L-4D5szfpo7eO639TBmnukw9y_X9h5Yc'\n","os.chdir(base_dir)\n","\n","print(\"Data should be located in folder: {}\".format(path_dict[\"RAW_DATA_PATH\"]))\n","assert os.path.isfile(os.path.join(path_dict[\"RAW_DATA_PATH\"], \"Task04_Hippocampus.zip\")) # check whether the file is correctly downloaded\n","assert os.path.isfile(os.path.join(path_dict[\"RAW_DATA_PATH\"], \"Task05_Prostate.zip\")) # check whether the file is correctly downloaded\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qZ6MpBLNYmk2"},"outputs":[],"source":["# only for in person workshops\n","\n","# unzip the files in the folder\n","os.chdir(path_dict[\"RAW_DATA_PATH\"])\n","!unzip -o Task04_Hippocampus.zip\n","!unzip -o Task05_Prostate.zip\n","!rm Task04_Hippocampus.zip\n","!rm Task05_Prostate.zip\n","os.chdir(base_dir)"]},{"cell_type":"markdown","metadata":{},"source":["### 6.1.2 Verification of data\n","Here we test, wether the data is saved in the correct folder structure."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rcALoM7JYmk4"},"outputs":[],"source":["print(\"Data should be located in folder: {}\".format(path_dict[\"RAW_DATA_PATH\"]))\n","assert os.path.exists(os.path.join(path_dict[\"RAW_DATA_PATH\"], \"Task04_Hippocampus\")) # check whether the file is correctly downloaded\n","assert os.path.exists(os.path.join(path_dict[\"RAW_DATA_PATH\"], \"Task05_Prostate\")) # check whether the file is correctly downloaded"]},{"cell_type":"markdown","metadata":{"id":"FEloIMv_qba4"},"source":["## 6.2 Training nnU-Net on the Decathlon Hippocampus Dataset\n","For this we will use already pre-existing scripts for handling the data."]},{"cell_type":"markdown","metadata":{},"source":["### 6.2.1 Dataset Conversion\n","The Decathlon datasets are 4D nifti files, for nnU-Net they have to be converted to 3D nifti files.\n","\n","For more information about dataset conversion see: [nnU-Net Dataset Formatting Instructions](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/dataset_format.md)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30089,"status":"ok","timestamp":1643125184634,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"LPjnXq4DPtzr","outputId":"9b9b5d3e-c634-455d-a262-2f8d5de837fd"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n"]}],"source":["!nnUNetv2_convert_MSD_dataset -i \"${RAW_DATA_PATH}/Task04_Hippocampus\""]},{"cell_type":"markdown","metadata":{},"source":["## 6.2.2 Extracting Rule Based Parameters\n","This will preprocess the dataset to allow fast training and saves it into the \"nnUNet_preprocessed\" folder.\n","Further rule based parameters will be extracted in the planning step."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":142590,"status":"ok","timestamp":1643125389006,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"O5gf6khI10WL","outputId":"72ec90ba-9638-4459-8c26-d212f944e922"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","hippocampus_367\n","hippocampus_189\n","hippocampus_185\n","hippocampus_294\n","hippocampus_064\n","hippocampus_150\n","hippocampus_314\n","hippocampus_107\n","hippocampus_243\n","hippocampus_304\n","hippocampus_099\n","hippocampus_007\n","hippocampus_343\n","hippocampus_050\n","hippocampus_023\n","hippocampus_332\n","hippocampus_204\n","hippocampus_269\n","hippocampus_353\n","hippocampus_296\n","hippocampus_220\n","hippocampus_105\n","hippocampus_394\n","hippocampus_232\n","hippocampus_279\n","hippocampus_277\n","hippocampus_253\n","hippocampus_288\n","hippocampus_178\n","hippocampus_097\n","hippocampus_123\n","hippocampus_298\n","hippocampus_318\n","hippocampus_308\n","hippocampus_222\n","hippocampus_042\n","hippocampus_166\n","hippocampus_320\n","hippocampus_176\n","hippocampus_152\n","hippocampus_265\n","hippocampus_375\n","hippocampus_095\n","hippocampus_142\n","hippocampus_017\n","hippocampus_197\n","hippocampus_015\n","hippocampus_052\n","hippocampus_330\n","hippocampus_216\n","hippocampus_322\n","hippocampus_133\n","hippocampus_109\n","hippocampus_068\n","hippocampus_351\n","hippocampus_386\n","hippocampus_087\n","hippocampus_316\n","hippocampus_195\n","hippocampus_033\n","hippocampus_074\n","hippocampus_019\n","hippocampus_251\n","hippocampus_286\n","hippocampus_230\n","hippocampus_089\n","hippocampus_341\n","hippocampus_174\n","hippocampus_040\n","hippocampus_170\n","hippocampus_164\n","hippocampus_158\n","hippocampus_199\n","hippocampus_245\n","hippocampus_070\n","hippocampus_257\n","hippocampus_228\n","hippocampus_212\n","hippocampus_345\n","hippocampus_101\n","hippocampus_044\n","hippocampus_292\n","hippocampus_302\n","hippocampus_181\n","hippocampus_048\n","hippocampus_238\n","hippocampus_363\n","hippocampus_001\n","hippocampus_144\n","hippocampus_025\n","hippocampus_361\n","hippocampus_336\n","hippocampus_135\n","hippocampus_338\n","hippocampus_263\n","hippocampus_162\n","hippocampus_039\n","hippocampus_125\n","hippocampus_261\n","hippocampus_236\n","hippocampus_035\n","hippocampus_349\n","hippocampus_300\n","hippocampus_380\n","hippocampus_290\n","hippocampus_046\n","hippocampus_210\n","hippocampus_259\n","hippocampus_282\n","hippocampus_249\n","hippocampus_160\n","hippocampus_037\n","hippocampus_390\n","hippocampus_146\n","hippocampus_310\n","hippocampus_359\n","hippocampus_169\n","hippocampus_334\n","hippocampus_060\n","hippocampus_127\n","hippocampus_280\n","hippocampus_226\n","hippocampus_373\n","hippocampus_224\n","hippocampus_114\n","hippocampus_003\n","hippocampus_083\n","hippocampus_156\n","hippocampus_154\n","hippocampus_091\n","hippocampus_355\n","hippocampus_093\n","hippocampus_014\n","hippocampus_172\n","hippocampus_056\n","hippocampus_234\n","hippocampus_058\n","hippocampus_326\n","hippocampus_328\n","hippocampus_193\n","hippocampus_011\n","hippocampus_177\n","hippocampus_148\n","hippocampus_006\n","hippocampus_295\n","hippocampus_366\n","hippocampus_084\n","hippocampus_231\n","hippocampus_268\n","hippocampus_077\n","hippocampus_175\n","hippocampus_106\n","hippocampus_141\n","hippocampus_317\n","hippocampus_233\n","hippocampus_331\n","hippocampus_215\n","hippocampus_075\n","hippocampus_051\n","hippocampus_065\n","hippocampus_041\n","hippocampus_217\n","hippocampus_184\n","hippocampus_340\n","hippocampus_264\n","hippocampus_108\n","hippocampus_165\n","hippocampus_132\n","hippocampus_321\n","hippocampus_374\n","hippocampus_333\n","hippocampus_194\n","hippocampus_219\n","hippocampus_008\n","hippocampus_287\n","hippocampus_385\n","hippocampus_221\n","hippocampus_274\n","hippocampus_250\n","hippocampus_223\n","hippocampus_319\n","hippocampus_143\n","hippocampus_130\n","hippocampus_260\n","hippocampus_096\n","hippocampus_309\n","hippocampus_350\n","hippocampus_094\n","hippocampus_207\n","hippocampus_389\n","hippocampus_387\n","hippocampus_360\n","hippocampus_242\n","hippocampus_252\n","hippocampus_378\n","hippocampus_376\n","hippocampus_067\n","hippocampus_289\n","hippocampus_053\n","hippocampus_203\n","hippocampus_188\n","hippocampus_352\n","hippocampus_205\n","hippocampus_276\n","hippocampus_004\n","hippocampus_297\n","hippocampus_299\n","hippocampus_303\n","hippocampus_088\n","hippocampus_098\n","hippocampus_305\n","hippocampus_368\n","hippocampus_104\n","hippocampus_020\n","hippocampus_244\n","hippocampus_372\n","hippocampus_155\n","hippocampus_126\n","hippocampus_149\n","hippocampus_337\n","hippocampus_327\n","hippocampus_311\n","hippocampus_026\n","hippocampus_036\n","hippocampus_057\n","hippocampus_229\n","hippocampus_190\n","hippocampus_138\n","hippocampus_136\n","hippocampus_157\n","hippocampus_329\n","hippocampus_227\n","hippocampus_038\n","hippocampus_381\n","hippocampus_102\n","hippocampus_354\n","hippocampus_090\n","hippocampus_145\n","hippocampus_393\n","hippocampus_161\n","hippocampus_325\n","hippocampus_356\n","hippocampus_045\n","hippocampus_173\n","hippocampus_124\n","hippocampus_225\n","hippocampus_235\n","hippocampus_034\n","hippocampus_370\n","hippocampus_024\n","hippocampus_092\n","hippocampus_335\n","hippocampus_383\n","hippocampus_301\n","hippocampus_171\n","hippocampus_358\n","hippocampus_049\n","hippocampus_248\n","hippocampus_180\n","hippocampus_163\n","\n","\n","\n"," Task004_Hippocampus\n","number of threads:  (8, 8) \n","\n","not using nonzero mask for normalization\n","Are we using the nonzero mask for normalizaion? OrderedDict([(0, False)])\n","the median shape of the dataset is  [36. 50. 35.]\n","the max shape in the dataset is  [47. 59. 43.]\n","the min shape in the dataset is  [24. 40. 31.]\n","we don't want feature maps smaller than  4  in the bottleneck\n","the transposed median shape of the dataset is  [36. 50. 35.]\n","generating configuration for 3d_fullres\n","{0: {'batch_size': 9, 'num_pool_per_axis': [3, 3, 3], 'patch_size': array([40, 56, 40]), 'median_patient_size_in_voxels': array([36, 50, 35]), 'current_spacing': array([1., 1., 1.]), 'original_spacing': array([1., 1., 1.]), 'do_dummy_2D_data_aug': False, 'pool_op_kernel_sizes': [[2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}}\n","transpose forward [0, 1, 2]\n","transpose backward [0, 1, 2]\n","Initializing to run preprocessing\n","npz folder: /content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_cropped_data/Task004_Hippocampus\n","output_folder: /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 48, 35)} \n","\n","1 2135\n","no resampling necessary\n","2 1343\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_017.npz\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 55, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 55, 36)} \n","\n","1 2289\n","no resampling necessary\n","no resampling necessary\n","2 1666\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_102.npz\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 35)} \n","\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 47, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 47, 32)} \n","\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 49, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 49, 38)} \n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 47, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 47, 35)} \n","\n","\n","no resampling necessary\n","no resampling necessary\n","1 1324\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 51, 35)} \n","\n","1 1867\n","2 1624\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_001.npz\n","1 1222\n","1 1488\n","2 1654\n","2 1583\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_092.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_035.npz\n","no resampling necessary\n","no resampling necessary\n","1 1390\n","2 1383\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_057.npz\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 48, 36)} \n","\n","2 1826\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_075.npz\n","1 1246\n","2 1622\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_045.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 47, 36)} \n","\n","1 1888\n","2 1468\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_019.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 47, 36)} \n","\n","1 1849\n","2 1660\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_036.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 36)} \n","after: no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 45, 47, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 36)} \n","\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 45, 47, 35)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 34)} \n","\n","1 1611\n","1 1762\n","2 1681\n","1 1984\n","2 1980\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_093.npz\n","2 1734\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_077.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_046.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 53, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 53, 34)} \n","\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 53, 35)} \n","\n","1 1334\n","1 2221\n","2 1793\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_058.npz\n","no resampling necessary\n","no resampling necessary\n","2 1596\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_104.npz\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 52, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 52, 34)} \n","\n","1 1550\n","2 1803\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_003.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 46, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 46, 36)} \n","\n","1 2146\n","2 1465\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_020.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 52, 36)} \n","\n","1 1832\n","2 1866\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_004.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 51, 34)} \n","\n","1 1578\n","2 1617\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_037.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 52, 38)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 47, 33)} \n","\n","1 1957\n","2 1315\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_048.npz\n","1 1832\n","2 1328\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_105.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 33)} \n","\n","1 1650\n","2 1721\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_083.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 52, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 52, 39)} \n","\n","1 1726\n","2 1683\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_060.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 50, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 50, 38)} \n","\n","1 2362\n","2 1667\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_094.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 35)} \n","\n","1 1748\n","2 1820\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_023.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 37)} \n","\n","1 1837\n","2 1721\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_038.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 35)} \n","\n","1 2314\n","2 1949\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_006.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 46, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 46, 34)} \n","\n","1 1712\n","2 1380\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_106.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 49, 34)} \n","\n","1 2191\n","2 1594\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_095.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 34)} \n","\n","1 1390\n","2 1760\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_084.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 51, 35)} \n","\n","1 1908\n","2 1820\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_049.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 53, 35)} \n","\n","1 2044\n","2 1616\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_064.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 38)} \n","\n","1 2011\n","2 2019\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_024.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 53, 34)} \n","\n","1 2038\n","2 1620\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_039.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 47, 34)} \n","\n","1 1842\n","2 1530\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_007.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 55, 35)} \n","\n","1 1933\n","2 1774\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_087.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 47, 34)} \n","\n","1 1890\n","2 1450\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_096.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 55, 35)} \n","\n","1 1911\n","2 2035\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_107.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 39)} \n","\n","1 1730\n","2 1920\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_065.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 38)} \n","\n","1 2023\n","2 1808\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_050.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 48, 35)} \n","\n","1 1896\n","2 1430\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_025.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 36)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 36)} \n","\n","1 1725\n","1 1906\n","2 1539\n","2 1523\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_040.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_008.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 42, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 42, 36)} \n","\n","1 1521\n","2 1290\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_067.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 37)} \n","\n","1 1354\n","2 1399\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_097.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 52, 40)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 52, 40)} \n","\n","1 2110\n","2 1768\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_088.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 36)} \n","\n","1 1863\n","2 1765\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_026.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 54, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 54, 33)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 36)} \n","\n","1 2059\n","1 1521\n","2 1588\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_051.npz\n","2 1881\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_108.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 50, 36)} \n","\n","1 1912\n","2 1544\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_011.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 37)} \n","\n","1 1515\n","2 1375\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_098.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 51, 36)} \n","\n","1 1777\n","2 1986\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_041.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 48, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 48, 33)} \n","\n","1 1855\n","2 1568\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_033.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 51, 34)} \n","\n","1 1725\n","2 1961\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_089.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 40, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 40, 36)} \n","\n","1 1733\n","2 1267\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_068.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 36)} \n","\n","1 1589\n","2 1630\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_109.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 52, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 52, 34)} \n","\n","1 1847\n","2 1514\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_052.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 27, 52, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 27, 52, 33)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 50, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 50, 39)} \n","\n","1 1252\n","2 1283\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_099.npz\n","1 2017\n","2 1605\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_014.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 50, 37)} \n","\n","1 2044\n","2 1957\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_090.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 37)} \n","\n","1 1871\n","2 1976\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_042.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 36)} \n","\n","1 1830\n","2 1545\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_034.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 50, 37)} \n","\n","1 1895\n","2 1555\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_070.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 37)} \n","\n","1 1650\n","2 1869\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_053.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 50, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 50, 38)} \n","\n","1 2459\n","2 1245\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_114.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 52, 36)} \n","\n","1 1888\n","2 1706\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_101.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 51, 42)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 51, 42)} \n","\n","1 1511\n","2 1308\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_015.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 51, 36)} \n","\n","1 1389\n","2 1672\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_091.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 48, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 48, 38)} \n","\n","1 1692\n","2 1528\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_044.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 55, 35)} \n","\n","1 1606\n","2 1531\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_124.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 47, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 47, 37)} \n","\n","1 1438\n","2 1562\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_074.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 47, 41)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 47, 41)} \n","\n","1 1965\n","2 1768\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_056.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 53, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 53, 32)} \n","\n","1 1712\n","2 1517\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_123.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 46, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 46, 32)} \n","\n","1 1171\n","2 1363\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_138.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 49, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 49, 37)} \n","\n","1 1605\n","2 1483\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_150.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 38)} \n","\n","1 1800\n","2 1620\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_162.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 53, 35)} \n","\n","1 1539\n","2 2018\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_173.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 42, 43)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 42, 43)} \n","\n","1 1657\n","2 1069\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_125.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 49, 35)} \n","\n","1 1841\n","2 1272\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_185.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 34)} \n","\n","1 1526\n","2 1301\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_203.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 45, 37)} \n","\n","1 1538\n","2 1292\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_219.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 44, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 44, 33)} \n","\n","1 1235\n","2 1479\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_141.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 36)} \n","\n","1 2121\n","2 1873\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_152.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 44, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 44, 47, 36)} \n","\n","1 1569\n","2 1934\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_163.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 55, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 55, 37)} \n","\n","1 1794\n","2 2153\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_174.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 54, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 54, 37)} \n","\n","1 1446\n","2 1928\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_188.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 48, 36)} \n","\n","1 1642\n","2 1231\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_204.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 44, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 44, 39)} \n","\n","1 1650\n","2 1495\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_126.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 43, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 43, 38)} \n","\n","1 1322\n","2 1375\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_142.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 46, 35)} \n","\n","1 1599\n","2 1658\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_154.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 45, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 45, 39)} \n","\n","1 1850\n","2 1080\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_220.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 47, 48, 41)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 47, 48, 41)} \n","\n","1 1700\n","2 2169\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_164.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 53, 35)} \n","\n","1 1781\n","2 1674\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_189.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 47, 33)} \n","\n","1 1575\n","2 1164\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_175.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 47, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 47, 32)} \n","\n","1 1314\n","2 1390\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_205.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 45, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 45, 32)} \n","\n","1 1193\n","2 1204\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_143.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 55, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 55, 38)} \n","\n","1 2094\n","2 1655\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_127.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 32)} \n","\n","1 1193\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 34)} \n","\n","2 1255\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_221.npz\n","1 2046\n","2 1509\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_155.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 53, 35)} \n","\n","1 1987\n","2 2087\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_207.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 35)} \n","\n","1 1791\n","2 1129\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_176.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 52, 37)} \n","\n","1 1665\n","2 1644\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_190.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 49, 34)} \n","\n","1 1544\n","2 1479\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_165.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 45, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 45, 34)} \n","\n","1 1227\n","2 1244\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_144.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 35)} \n","\n","1 1705\n","2 1580\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_130.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 34)} \n","\n","1 1691\n","2 993\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_222.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 52, 36)} \n","\n","1 2102\n","2 1498\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_156.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 50, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 50, 33)} \n","\n","1 1351\n","2 1357\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_193.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 34)} \n","\n","1 1641\n","2 1354\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_210.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 53, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 53, 36)} \n","\n","1 2074\n","2 1462\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_145.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 49, 36)} \n","after: no resampling necessary\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 49, 36)} \n","\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 35)} \n","\n","1 1402\n","no resampling necessary\n","2 1625\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_166.npz\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 44, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 44, 33)} \n","\n","1 1054\n","2 1539\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_177.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 50, 36)} \n","\n","1 1572\n","2 1679\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_132.npz\n","1 1577\n","2 1925\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_223.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 36)} \n","\n","1 1444\n","2 1908\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_157.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 50, 35)} \n","\n","1 1361\n","2 1533\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_194.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 56, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 56, 35)} \n","\n","1 1746\n","2 1836\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_212.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 41, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 41, 39)} \n","\n","1 1899\n","2 1510\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_133.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 45, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 45, 36)} \n","\n","1 1363\n","2 1496\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_169.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 44, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 44, 35)} \n","\n","1 1224\n","2 1490\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_178.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 52, 38)} \n","\n","1 1874\n","2 1538\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_158.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 48, 37)} \n","\n","1 2033\n","2 1787\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_224.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 51, 36)} \n","\n","1 2017\n","2 1505\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_146.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 53, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 53, 33)} \n","\n","1 1831\n","2 1830\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_195.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 49, 35)} \n","\n","1 1600\n","2 1733\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_215.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 32)} \n","\n","1 1363\n","2 1266\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_135.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 34)} \n","\n","1 1311\n","2 1559\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_170.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 48, 34)} \n","\n","1 1689\n","2 1256\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_148.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 45, 37)} \n","\n","1 1397\n","2 1281\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_180.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 51, 34)} \n","\n","1 1682\n","2 1464\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_160.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 53, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 53, 33)} \n","\n","1 1119\n","2 1356\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_225.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 38)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 32)} \n","\n","1 1784\n","2 1593\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_197.npz\n","1 1882\n","2 1560\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_216.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 49, 34)} \n","\n","1 1445\n","2 1368\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_136.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 56, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 56, 35)} \n","\n","1 1849\n","2 1825\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_171.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 51, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 51, 32)} \n","\n","1 1364\n","2 1182\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_226.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 33)} \n","after: no resampling necessary\n","no resampling necessary\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 33)} \n","\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 51, 35)} \n","\n","1 1963\n","1 1775\n","2 1942\n","2 1729\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_181.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_161.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 49, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 49, 33)} \n","\n","1 1620\n","2 1523\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_149.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 27, 53, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 27, 53, 38)} \n","\n","1 2020\n","2 1082\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_217.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 48, 37)} \n","\n","1 2033\n","2 1638\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_228.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 56, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 56, 34)} \n","\n","1 1981\n","2 1942\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_172.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 47, 36)} \n","\n","1 1979\n","2 1557\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_227.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 52, 37)} \n","\n","1 1158\n","2 1412\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_199.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 37)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 56, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 56, 37)} \n","\n","1 2302\n","2 1685\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_238.npz\n","1 1948\n","2 1657\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_184.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 55, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 55, 37)} \n","\n","1 2060\n","2 1515\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_252.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 34)} \n","\n","1 1437\n","2 1617\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_268.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 50, 37)} \n","\n","1 1766\n","2 1934\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_287.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 50, 33)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 50, 33)} \n","\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 50, 37)} \n","\n","1 1458\n","no resampling necessary\n","no resampling necessary\n","1 1798\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 52, 34)} \n","after: 2 1059\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 52, 34)} \n","\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_298.npz\n","1 1782\n","2 1830\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_309.npz\n","2 1719\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_229.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 34)} \n","\n","1 2343\n","2 1480\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_253.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 46, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 46, 34)} \n","\n","1 1601\n","2 1349\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_321.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 35)} \n","\n","1 1725\n","2 1714\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_269.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 34)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 54, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 54, 32)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 38)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 52, 35)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 50, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 50, 38)} \n","\n","1 1850\n","1 1517\n","2 1685\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_299.npz\n","1 2477\n","2 1906\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_242.npz\n","1 1556\n","2 1609\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_230.npz\n","1 1823\n","2 1950\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_310.npz\n","2 1995\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_288.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 40, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 40, 35)} \n","\n","1 1491\n","2 1174\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_274.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 47, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 47, 38)} \n","\n","1 1979\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 34)} \n","\n","1 1188\n","2 1471\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_322.npz\n","2 1675\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_257.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 47, 33)} \n","\n","1 1179\n","2 1468\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_231.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 35)} \n","\n","1 1282\n","2 1456\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_289.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 51, 35)} \n","\n","1 1898\n","2 1936\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_325.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 53, 34)} \n","\n","1 1846\n","2 1558\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_300.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 24, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 24, 53, 34)} \n","\n","1 1421\n","2 1535\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_243.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 50, 35)} \n","\n","1 2056\n","2 1587\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_276.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 51, 33)} \n","\n","1 1338\n","2 1582\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_259.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 37)} \n","\n","1 1565\n","2 1636\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_311.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 44, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 44, 36)} \n","\n","1 1514\n","2 1349\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_232.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 35)} \n","\n","1 1459\n","2 1708\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_290.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 53, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 53, 38)} \n","\n","1 1697\n","2 1511\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_244.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 53, 35)} \n","\n","1 1379\n","2 1718\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_260.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 31)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 31)} \n","\n","1 1595\n","2 1865\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_301.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 53, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 53, 37)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 49, 36)} \n","\n","1 1697\n","1 2135\n","2 1848\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_326.npz\n","2 1246\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_314.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 59, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 59, 33)} \n","\n","1 1695\n","2 1609\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_277.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 48, 35)} \n","\n","1 1630\n","2 1695\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_245.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 33)} \n","\n","1 1593\n","2 1574\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_233.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 46, 35)} \n","\n","1 2209\n","2 1647\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_302.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 38)} \n","\n","1 1911\n","2 1568\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_292.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 58, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 58, 36)} \n","\n","1 2399\n","2 1672\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_261.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 37)} \n","\n","1 1580\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 27, 54, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 27, 54, 36)} \n","\n","1 2092\n","2 1551\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_327.npz\n","2 1515\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_316.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 48, 35)} \n","\n","1 1758\n","2 1536\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_303.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 38)} \n","\n","1 1549\n","2 1667\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_234.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 50, 36)} \n","\n","1 1887\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 50, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 50, 34)} \n","\n","1 1261\n","2 1121\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_279.npz\n","2 1551\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_248.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 44, 44, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 44, 44, 35)} \n","\n","1 1746\n","2 1502\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_294.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 51, 33)} \n","\n","1 1656\n","2 1629\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_317.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 54, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 54, 38)} \n","\n","1 2371\n","2 1586\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_328.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 36)} \n","\n","1 1647\n","2 1887\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_263.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 32)} \n","\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 47, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 47, 37)} \n","\n","1 1367\n","1 1784\n","2 1246\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_280.npz\n","2 1565\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_249.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 58, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 58, 37)} \n","\n","1 1479\n","2 1561\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_235.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 48, 36)} \n","\n","1 2016\n","2 1520\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_304.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 53, 35)} \n","\n","1 1907\n","2 1721\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_295.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 53, 34)} \n","\n","1 1937\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 37)} \n","\n","1 2029\n","2 1738\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_329.npz\n","2 1487\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_318.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 38)} \n","\n","1 1934\n","2 1908\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_264.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 49, 34)} \n","\n","1 1649\n","2 1380\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_305.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 51, 35)} \n","\n","1 1899\n","2 1532\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_250.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 52, 37)} \n","\n","1 1133\n","2 1283\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_282.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 57, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 57, 37)} \n","\n","1 1595\n","2 1494\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_236.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 54, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 54, 35)} \n","\n","1 2098\n","2 1820\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_296.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 55, 35)} \n","\n","1 2232\n","2 1765\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_330.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 54, 31)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 54, 31)} \n","\n","1 1455\n","2 1654\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_265.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 38)} \n","\n","1 2011\n","2 1643\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_308.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 33)} \n","\n","1 1380\n","2 1042\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_319.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 35)} \n","\n","1 1803\n","2 1524\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_332.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 51, 34)} \n","\n","1 1551\n","2 1235\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_297.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 46, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 46, 45, 37)} \n","\n","1 1562\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 58, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 58, 36)} \n","\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 35)} \n","\n","2 1610\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_286.npz\n","1 1561\n","2 1633\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_331.npz\n","1 2221\n","2 1354\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_251.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 47, 33)} \n","\n","1 1054\n","2 1397\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_320.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 45, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 45, 32)} \n","\n","1 1067\n","2 1551\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_343.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 36)} \n","\n","1 1863\n","2 1633\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_356.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 35)} \n","\n","1 1597\n","2 1801\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_370.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 55, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 55, 33)} \n","\n","1 1777\n","2 1643\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_383.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 46, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 46, 33)} \n","\n","1 1192\n","2 1443\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_333.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 49, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 49, 32)} \n","\n","1 1685\n","2 1295\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_345.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 50, 35)} \n","\n","1 1519\n","2 1368\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_358.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 50, 36)} \n","\n","1 1751\n","2 1732\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_372.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 50, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 50, 34)} \n","\n","1 1217\n","2 1373\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_349.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 35)} \n","\n","1 2113\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 47, 34)} \n","\n","2 1284\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_385.npz\n","1 1242\n","2 1436\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_334.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 49, 35)} \n","\n","1 1262\n","2 1372\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_359.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 47, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 47, 32)} \n","\n","1 1263\n","2 1269\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_335.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 45, 37)} \n","\n","1 1959\n","2 1249\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_386.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 49, 35)} \n","\n","1 1475\n","2 1467\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_350.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 49, 34)} \n","\n","1 1882\n","2 1807\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_373.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 34)} \n","\n","1 1298\n","2 1633\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_360.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 35)} \n","\n","1 1822\n","2 1902\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_351.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 47, 34)} \n","\n","1 1298\n","2 1295\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_336.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 48, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 48, 38)} \n","\n","1 2123\n","2 1754\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_374.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 51, 33)} \n","\n","1 1509\n","2 1571\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_387.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 50, 36)} \n","\n","1 1733\n","2 1372\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_361.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 38)} \n","\n","1 1966\n","2 1713\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_352.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 49, 34)} \n","\n","1 1763\n","2 1184\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_389.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 54, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 54, 32)} \n","\n","1 1686\n","2 1536\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_375.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 44, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 44, 33)} \n","\n","1 1551\n","2 1702\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_337.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 32)} \n","\n","1 1415\n","2 1340\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_353.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 52, 38)} \n","\n","1 1838\n","2 1668\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_363.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 55, 35)} \n","\n","1 1625\n","2 1956\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_376.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 43, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 43, 37)} \n","\n","1 2007\n","2 1616\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_338.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 38)} \n","\n","1 1886\n","2 1366\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_390.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 47, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 47, 37)} \n","\n","1 2482\n","2 1497\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_366.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 50, 36)} \n","\n","1 1529\n","2 1383\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_354.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 35)} \n","\n","1 1180\n","2 1577\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_378.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 36)} \n","\n","1 1864\n","2 1818\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_393.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 46, 35)} \n","\n","1 1592\n","2 1618\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_340.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 47, 33)} \n","\n","1 1819\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 57, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 57, 36)} \n","\n","1 2212\n","2 1516\n","2 1939\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_355.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_367.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 46, 35)} \n","\n","1 1730\n","2 1705\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_380.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 55, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 55, 38)} \n","\n","1 2593\n","2 1808\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_368.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 48, 34)} \n","\n","1 1331\n","2 1257\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_341.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 52, 36)} \n","\n","1 1987\n","2 1827\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_394.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 33)} \n","\n","1 1550\n","2 1602\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_stage0/hippocampus_381.npz\n","not using nonzero mask for normalization\n","Are we using the nonzero maks for normalizaion? OrderedDict([(0, False)])\n","the median shape of the dataset is  [36. 50. 35.]\n","the max shape in the dataset is  [47. 59. 43.]\n","the min shape in the dataset is  [24. 40. 31.]\n","we don't want feature maps smaller than  4  in the bottleneck\n","the transposed median shape of the dataset is  [36. 50. 35.]\n","[{'batch_size': 366, 'num_pool_per_axis': [3, 3], 'patch_size': array([56, 40]), 'median_patient_size_in_voxels': array([36, 50, 35]), 'current_spacing': array([1., 1., 1.]), 'original_spacing': array([1., 1., 1.]), 'pool_op_kernel_sizes': [[2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3]], 'do_dummy_2D_data_aug': False}]\n","Initializing to run preprocessing\n","npz folder: /content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_cropped_data/Task004_Hippocampus\n","output_folder: /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 48, 36)} \n","\n","normalization...\n","normalization done\n","1 1246\n","2 1622\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_045.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 51, 35)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 47, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 47, 35)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 48, 35)} \n","\n","normalization...\n","normalization done\n","1 2135\n","2 1343\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 55, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 55, 36)} \n","\n","normalization...\n","normalization done\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 47, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 47, 32)} \n","\n","normalization...\n","normalization done\n","1 1390\n","2 1383\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_057.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_017.npz\n","1 1867\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 35)} \n","\n","normalization...\n","1 1222\n","normalization done\n","1 1324\n","2 1826\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_075.npz\n","2 1624\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_001.npz\n","1 2289\n","2 1666\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_102.npz\n","2 1583\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_035.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 49, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 49, 38)} \n","\n","normalization...\n","normalization done\n","1 1488\n","2 1654\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_092.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 47, 36)} \n","\n","normalization...\n","normalization done\n","1 1849\n","2 1660\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_036.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 53, 34)} \n","\n","normalization...\n","normalization done\n","1 1334\n","2 1793\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_058.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 45, 47, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 45, 47, 35)} \n","\n","normalization...\n","normalization done\n","1 1984\n","2 1734\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_077.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 36)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 47, 36)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","normalization done\n","normalization done\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 53, 35)} \n","\n","normalization...\n","normalization done\n","1 2221\n","2 1596\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_104.npz\n","1 1611\n","no resampling necessary\n","1 1888\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 52, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 52, 34)} \n","\n","normalization...\n","2 1681\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_046.npz\n","normalization done\n","2 1468\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_019.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 34)} \n","\n","normalization...\n","1 1550\n","2 1803\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_003.npz\n","normalization done\n","1 1762\n","2 1980\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_093.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 52, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 52, 39)} \n","\n","normalization...\n","normalization done\n","1 1726\n","no resampling necessary\n","no resampling necessary\n","2 1683\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 51, 34)} \n","after: saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_060.npz\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 51, 34)} \n","\n","normalization...\n","normalization done\n","1 1578\n","2 1617\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_037.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 52, 38)} \n","\n","normalization...\n","normalization done\n","1 1957\n","2 1315\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_048.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 46, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 46, 36)} \n","\n","normalization...\n","normalization done\n","1 2146\n","2 1465\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_020.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 47, 33)} \n","\n","normalization...\n","normalization done\n","1 1832\n","2 1328\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_105.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 52, 36)} \n","\n","normalization...\n","normalization done\n","1 1832\n","2 1866\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_004.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 50, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 50, 38)} \n","\n","normalization...\n","normalization done\n","1 2362\n","2 1667\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_094.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 53, 35)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 37)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 33)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1837\n","1 1650\n","1 2044\n","2 1721\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_083.npz\n","2 1616\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_064.npz\n","2 1721\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_038.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 51, 35)} \n","\n","normalization...\n","normalization done\n","1 1908\n","2 1820\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_049.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 53, 34)} \n","\n","normalization...\n","normalization done\n","1 2038\n","2 1620\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_039.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 46, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 46, 34)} \n","no resampling necessary\n","no resampling necessary\n","\n","normalization...\n","normalization done\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 35)} \n","\n","normalization...\n","1 1712\n","normalization done\n","1 2314\n","2 1380\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_106.npz\n","2 1949\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_006.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 35)} \n","\n","normalization...\n","normalization done\n","1 1748\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 39)} \n","after: 2 1820\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_023.npz\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 39)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 38)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 49, 34)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 34)} \n","\n","\n","normalization...\n","normalization...\n","normalization done\n","1 1390\n","2 1760\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_084.npz\n","normalization done\n","1 2023\n","2 1808\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_050.npz\n","1 1730\n","2 1920\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_065.npz\n","normalization done\n","1 2191\n","2 1594\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_095.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 47, 34)} \n","\n","normalization...\n","normalization done\n","1 1842\n","2 1530\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_007.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 36)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 55, 35)} \n","\n","normalization...\n","1 1906\n","2 1539\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_040.npz\n","normalization done\n","1 1911\n","2 2035\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_107.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 38)} \n","\n","normalization...\n","normalization done\n","1 2011\n","2 2019\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_024.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 54, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 54, 33)} \n","\n","normalization...\n","normalization done\n","1 1521\n","2 1588\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_051.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 55, 35)} \n","\n","normalization...\n","normalization done\n","1 1933\n","2 1774\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_087.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 42, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 42, 36)} \n","\n","normalization...\n","normalization done\n","1 1521\n","2 1290\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_067.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 47, 34)} \n","\n","normalization...\n","normalization done\n","1 1890\n","2 1450\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_096.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 52, 40)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 52, 40)} \n","\n","normalization...\n","normalization done\n","1 2110\n","2 1768\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_088.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 36)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 51, 36)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 48, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 52, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 52, 34)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 36)} \n","\n","normalization...\n","1 1725\n","2 1523\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_008.npz\n","normalization done\n","normalization done\n","normalization done\n","1 2059\n","2 1881\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_108.npz\n","1 1847\n","2 1514\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_052.npz\n","1 1777\n","1 1896\n","2 1986\n","2 1430\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_041.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_025.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 40, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 40, 36)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 51, 34)} \n","after: 1 1733\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 51, 34)} \n","\n","normalization...\n","2 1267\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_068.npz\n","normalization done\n","1 1725\n","2 1961\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_089.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 37)} \n","\n","normalization...\n","normalization done\n","1 1354\n","2 1399\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_097.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 36)} \n","\n","normalization...\n","normalization done\n","1 1589\n","2 1630\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_109.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 37)} \n","\n","normalization...\n","normalization done\n","1 1871\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 36)} \n","\n","normalization...\n","normalization done\n","1 1863\n","2 1765\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_026.npz\n","2 1976\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_042.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 37)} \n","\n","normalization...\n","normalization done\n","1 1650\n","2 1869\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_053.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 50, 36)} \n","\n","normalization...\n","normalization done\n","1 1912\n","2 1544\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_011.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 37)} \n","after: no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 37)} \n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 50, 37)} \n","\n","normalization...\n","normalization done\n","\n","normalization...\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 50, 37)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 2044\n","1 1515\n","2 1375\n","1 1895\n","2 1555\n","2 1957\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_098.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_070.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_090.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 50, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 50, 39)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 50, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 50, 38)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 48, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 48, 33)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 47, 41)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 47, 41)} \n","\n","normalization...\n","normalization done\n","normalization done\n","normalization done\n","1 1965\n","normalization done\n","1 1855\n","no resampling necessary\n","1 2017\n","2 1568\n","no resampling necessary\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_033.npz\n","1 2459\n","2 1768\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_056.npz\n","2 1245\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_114.npz\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 48, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 48, 38)} \n","\n","normalization...\n","normalization done\n","2 1605\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_014.npz\n","1 1692\n","2 1528\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_044.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 27, 52, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 27, 52, 33)} \n","\n","normalization...\n","normalization done\n","1 1252\n","2 1283\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_099.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 47, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 47, 37)} \n","\n","normalization...\n","normalization done\n","1 1438\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 51, 36)} \n","\n","normalization...\n","2 1562\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_074.npz\n","normalization done\n","1 1389\n","2 1672\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_091.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 55, 35)} \n","\n","normalization...\n","normalization done\n","1 1606\n","2 1531\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_124.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 36)} \n","\n","normalization...\n","normalization done\n","1 1830\n","2 1545\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_034.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 53, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 53, 32)} \n","\n","normalization...\n","normalization done\n","1 1712\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 46, 32)} \n","after: 2 1517\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_123.npz\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 46, 32)} \n","\n","normalization...\n","normalization done\n","1 1171\n","2 1363\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_138.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 51, 42)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 51, 42)} \n","\n","normalization...\n","normalization done\n","1 1511\n","2 1308\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_015.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 49, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 49, 37)} \n","\n","normalization...\n","normalization done\n","1 1605\n","2 1483\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_150.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 38)} \n","\n","normalization...\n","normalization done\n","1 1800\n","2 1620\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_162.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 52, 36)} \n","\n","normalization...\n","normalization done\n","1 1888\n","2 1706\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_101.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 42, 43)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 42, 43)} \n","\n","normalization...\n","normalization done\n","1 1657\n","2 1069\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_125.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 53, 35)} \n","\n","normalization...\n","normalization done\n","1 1539\n","2 2018\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_173.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 49, 35)} \n","\n","normalization...\n","normalization done\n","1 1841\n","2 1272\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_185.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 44, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 44, 33)} \n","\n","normalization...\n","normalization done\n","1 1235\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 34)} \n","\n","normalization...\n","normalization done\n","2 1479\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_141.npz\n","1 1526\n","2 1301\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_203.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 36)} \n","\n","normalization...\n","normalization done\n","1 2121\n","2 1873\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_152.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 44, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 44, 47, 36)} \n","\n","normalization...\n","normalization done\n","1 1569\n","2 1934\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_163.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 45, 37)} \n","\n","normalization...\n","normalization done\n","1 1538\n","2 1292\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_219.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 55, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 55, 37)} \n","\n","normalization...\n","normalization done\n","1 1794\n","2 2153\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_174.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 44, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 44, 39)} \n","\n","normalization...\n","normalization done\n","1 1650\n","2 1495\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_126.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 48, 36)} \n","after: no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 54, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 54, 37)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 48, 36)} \n","\n","normalization...\n","normalization done\n","\n","normalization...\n","normalization done\n","1 1642\n","1 1446\n","2 1928\n","2 1231\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_204.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_188.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 47, 48, 41)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 47, 48, 41)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","1 1700\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 47, 33)} \n","\n","2 2169\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_164.npz\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 46, 35)} \n","\n","normalization...\n","normalization done\n","1 1575\n","1 1599\n","2 1164\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_175.npz\n","2 1658\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_154.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 43, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 43, 38)} \n","\n","normalization...\n","normalization done\n","1 1322\n","2 1375\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_142.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 53, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 45, 39)} \n","after: 1 1781\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 45, 39)} \n","\n","normalization...\n","normalization done\n","2 1674\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 47, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 47, 32)} \n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 55, 38)} \n","after: saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_189.npz\n","\n","normalization...\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 55, 38)} \n","\n","normalization done\n","normalization...\n","1 1850\n","normalization done\n","2 1080\n","1 1314\n","1 2094\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_220.npz\n","2 1390\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_205.npz\n","2 1655\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_127.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 45, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 45, 32)} \n","\n","normalization...\n","normalization done\n","1 1193\n","2 1204\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 49, 34)} \n","\n","normalization...\n","normalization done\n","1 1544\n","2 1479\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_143.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 35)} \n","\n","normalization...\n","normalization done\n","1 1791\n","2 1129\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_165.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 53, 34)} \n","\n","normalization...\n","normalization done\n","1 2046\n","2 1509\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_176.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_155.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 52, 37)} \n","\n","normalization...\n","normalization done\n","1 1665\n","2 1644\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_190.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 53, 35)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 53, 35)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 32)} \n","\n","\n","normalization...\n","normalization...\n","normalization done\n","normalization done\n","1 1987\n","2 2087\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_207.npz\n","1 1705\n","2 1580\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_130.npz\n","1 1193\n","2 1255\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_221.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 52, 36)} \n","\n","normalization...\n","normalization done\n","1 2102\n","2 1498\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_156.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 49, 36)} \n","\n","normalization...\n","normalization done\n","1 1402\n","2 1625\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_166.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 45, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 45, 34)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 44, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 44, 33)} \n","\n","normalization...\n","normalization done\n","1 1054\n","1 1227\n","2 1244\n","2 1539\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_177.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_144.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 50, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 50, 33)} \n","\n","normalization...\n","normalization done\n","1 1351\n","2 1357\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_193.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 34)} \n","\n","normalization...\n","normalization done\n","1 1691\n","2 993\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_222.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 34)} \n","\n","normalization...\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 50, 36)} \n","after: normalization done\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 50, 36)} \n","\n","normalization...\n","1 1641\n","2 1354\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_210.npz\n","normalization done\n","1 1572\n","2 1679\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_132.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 53, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 53, 36)} \n","\n","normalization...\n","normalization done\n","1 2074\n","2 1462\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_145.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 44, 35)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 36)} \n","after: no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 45, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 36)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 44, 35)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 45, 36)} \n","\n","normalization...\n","\n","normalization...\n","normalization done\n","normalization done\n","\n","normalization...\n","normalization done\n","1 1224\n","1 1363\n","2 1496\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_169.npz\n","1 1444\n","2 1490\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_178.npz\n","2 1908\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_157.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 52, 35)} \n","\n","normalization...\n","normalization done\n","1 1577\n","2 1925\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_223.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 56, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 56, 35)} \n","\n","normalization...\n","normalization done\n","1 1746\n","2 1836\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_212.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 50, 35)} \n","\n","normalization...\n","normalization done\n","1 1361\n","2 1533\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_194.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 41, 39)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 41, 39)} \n","\n","normalization...\n","normalization done\n","1 1899\n","2 1510\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_133.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 34)} \n","\n","normalization...\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 52, 38)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1311\n","1 1874\n","2 1538\n","2 1559\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_158.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_170.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 51, 36)} \n","after: no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 51, 36)} \n","\n","normalization...\n","normalization done\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 45, 37)} \n","\n","normalization...\n","normalization done\n","1 2017\n","1 1397\n","2 1505\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_146.npz\n","2 1281\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_180.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 48, 37)} \n","\n","normalization...\n","normalization done\n","1 2033\n","2 1787\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_224.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 49, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 49, 32)} \n","\n","normalization...\n","normalization done\n","1 1363\n","2 1266\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_135.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 53, 33)} \n","after: no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 49, 35)} \n","\n","normalization...\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 53, 33)} \n","normalization done\n","\n","normalization...\n","normalization done\n","1 1600\n","2 1733\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_215.npz\n","1 1831\n","2 1830\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_195.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 56, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 56, 35)} \n","\n","normalization...\n","normalization done\n","1 1849\n","2 1825\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_171.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 33)} \n","\n","normalization...\n","normalization done\n","1 1963\n","2 1729\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_181.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 51, 34)} \n","\n","normalization...\n","normalization done\n","1 1682\n","2 1464\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_160.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 48, 34)} \n","\n","normalization...\n","normalization done\n","1 1689\n","2 1256\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_148.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 56, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 56, 34)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 53, 33)} \n","after: normalization done\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 53, 33)} \n","\n","normalization...\n","normalization done\n","1 1981\n","1 1119\n","2 1942\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_172.npz\n","2 1356\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_225.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 32)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 51, 35)} \n","\n","normalization...\n","normalization done\n","1 1882\n","2 1560\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_216.npz\n","1 1775\n","2 1942\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_161.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 49, 34)} \n","\n","normalization...\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 37)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1445\n","1 1948\n","2 1657\n","2 1368\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_136.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_184.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 38)} \n","\n","normalization...\n","normalization done\n","1 1784\n","2 1593\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_197.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 49, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 49, 33)} \n","\n","normalization...\n","normalization done\n","1 1620\n","2 1523\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_149.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 27, 53, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 27, 53, 38)} \n","\n","normalization...\n","normalization done\n","1 2020\n","2 1082\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_217.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 48, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 48, 37)} \n","\n","normalization...\n","normalization done\n","1 2033\n","2 1638\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_228.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 56, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 56, 37)} \n","\n","normalization...\n","normalization done\n","1 2302\n","2 1685\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_238.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 51, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 51, 32)} \n","\n","normalization...\n","normalization done\n","1 1364\n","2 1182\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_226.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 52, 37)} \n","\n","normalization...\n","normalization done\n","1 1158\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 34)} \n","\n","normalization...\n","normalization done\n","1 1437\n","2 1412\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 26, 55, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 26, 55, 37)} \n","\n","normalization...\n","normalization done\n","1 2060\n","2 1515\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_252.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_199.npz\n","2 1617\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_268.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 50, 37)} \n","\n","normalization...\n","normalization done\n","1 1766\n","2 1934\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_287.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 47, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 47, 36)} \n","\n","normalization...\n","normalization done\n","1 1979\n","2 1557\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_227.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 50, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 50, 33)} \n","\n","normalization...\n","normalization done\n","1 1458\n","2 1719\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_229.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 38)} \n","\n","normalization...\n","normalization done\n","1 2477\n","2 1906\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_242.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 34)} \n","\n","normalization...\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 35)} \n","\n","normalization...\n","normalization done\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 50, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 50, 37)} \n","\n","normalization...\n","1 1725\n","2 1714\n","normalization done\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_269.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 52, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 52, 34)} \n","\n","normalization...\n","normalization done\n","1 2343\n","2 1480\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_253.npz\n","1 1798\n","2 1059\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_298.npz\n","1 1782\n","2 1830\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_309.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 50, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 50, 38)} \n","\n","normalization...\n","normalization done\n","1 1850\n","2 1995\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_288.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 52, 35)} \n","\n","normalization...\n","normalization done\n","1 1823\n","2 1950\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_310.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 24, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 24, 53, 34)} \n","\n","normalization...\n","normalization done\n","1 1421\n","2 1535\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_243.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 34)} \n","\n","normalization...\n","normalization done\n","1 1556\n","2 1609\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_230.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 46, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 54, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 54, 32)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 46, 34)} \n","\n","normalization...\n","\n","normalization...\n","normalization done\n","normalization done\n","1 1517\n","1 1601\n","2 1685\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_299.npz\n","2 1349\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_321.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 40, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 40, 35)} \n","\n","normalization...\n","normalization done\n","1 1491\n","2 1174\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_274.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 34)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 35)} \n","\n","normalization...\n","normalization done\n","1 1282\n","1 1188\n","2 1456\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_289.npz\n","2 1675\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_257.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 37)} \n","\n","normalization...\n","normalization done\n","1 1565\n","2 1636\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_311.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 50, 35)} \n","\n","normalization...\n","normalization done\n","1 2056\n","2 1587\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_276.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 47, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 47, 38)} \n","\n","normalization...\n","normalization done\n","1 1979\n","2 1471\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_322.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 51, 33)} \n","\n","normalization...\n","normalization done\n","1 1338\n","2 1582\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_259.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 53, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 49, 35)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 53, 34)} \n","\n","\n","normalization...\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 53, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 53, 38)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1459\n","2 1708\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_290.npz\n","normalization done\n","1 1846\n","2 1558\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_300.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 47, 33)} \n","\n","normalization...\n","normalization done\n","1 1179\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 53, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 53, 37)} \n","\n","normalization...\n","normalization done\n","1 1697\n","2 1246\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_314.npz\n","1 1697\n","2 1511\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_244.npz\n","2 1468\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_231.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 59, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 59, 33)} \n","\n","normalization...\n","normalization done\n","1 1695\n","2 1609\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_277.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 51, 35)} \n","\n","normalization...\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 38)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1898\n","1 1911\n","2 1936\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_325.npz\n","2 1568\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_292.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 44, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 44, 36)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","1 1514\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 37)} \n","\n","normalization...\n","2 1349\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_232.npz\n","normalization done\n","1 1580\n","2 1515\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_316.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 50, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 50, 34)} \n","\n","normalization...\n","normalization done\n","1 1261\n","2 1121\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_279.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 31)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 31)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 53, 35)} \n","\n","\n","normalization...\n","normalization...\n","normalization done\n","normalization done\n","1 1595\n","2 1865\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_301.npz\n","1 1379\n","2 1718\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_260.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 48, 35)} \n","\n","normalization...\n","normalization done\n","1 1630\n","2 1695\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_245.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 49, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 49, 36)} \n","\n","normalization...\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 33)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1593\n","1 2135\n","2 1574\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_233.npz\n","2 1848\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_326.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 44, 44, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 44, 44, 35)} \n","\n","normalization...\n","normalization done\n","1 1746\n","2 1502\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_294.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 47, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 47, 37)} \n","\n","normalization...\n","normalization done\n","1 1367\n","2 1246\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_280.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 51, 33)} \n","\n","normalization...\n","normalization done\n","1 1656\n","2 1629\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_317.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 46, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 58, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 58, 36)} \n","\n","normalization...\n","normalization done\n","1 2399\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 50, 36)} \n","\n","normalization...\n","normalization done\n","1 2209\n","2 1647\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_302.npz\n","2 1672\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_261.npz\n","1 1887\n","2 1551\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_248.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 48, 35)} \n","\n","normalization...\n","normalization done\n","1 1758\n","2 1536\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_303.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 27, 54, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 27, 54, 36)} \n","\n","normalization...\n","normalization done\n","1 2092\n","2 1551\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_327.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 32)} \n","\n","normalization...\n","normalization done\n","1 1784\n","2 1565\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_249.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 52, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 52, 37)} \n","\n","normalization...\n","normalization done\n","1 1133\n","2 1283\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_282.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 36)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 53, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 53, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 49, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 49, 38)} \n","\n","normalization...\n","normalization done\n","1 1907\n","2 1721\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_295.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 37)} \n","\n","normalization...\n","normalization done\n","1 1549\n","2 1667\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_234.npz\n","1 1647\n","2 1887\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_263.npz\n","1 2029\n","2 1487\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_318.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 48, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 48, 36)} \n","\n","normalization...\n","normalization done\n","1 2016\n","2 1520\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_304.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 54, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 54, 35)} \n","\n","normalization...\n","normalization done\n","1 2098\n","2 1820\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_296.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 54, 38)} \n","after: no resampling necessary\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 54, 38)} \n","\n","normalization...\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 58, 37)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 48, 33)} \n","after: normalization done\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 46, 45, 37)} \n","after: no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 58, 37)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 46, 45, 37)} \n","1 2371\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 48, 33)} \n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 51, 35)} \n","\n","normalization...\n","\n","2 1586\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_328.npz\n","\n","normalization...\n","normalization...\n","normalization done\n","normalization done\n","1 1479\n","2 1561\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_235.npz\n","normalization done\n","\n","normalization...\n","1 1562\n","normalization done\n","2 1610\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_286.npz\n","1 1380\n","1 1899\n","2 1042\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_319.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 38)} \n","\n","normalization...\n","normalization done\n","1 1934\n","2 1532\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_250.npz\n","2 1908\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_264.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 49, 34)} \n","\n","normalization...\n","normalization done\n","1 1649\n","2 1380\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_305.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 28, 58, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 28, 58, 36)} \n","\n","normalization...\n","normalization done\n","1 2221\n","2 1354\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_251.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 47, 33)} \n","\n","normalization...\n","normalization done\n","1 1054\n","2 1397\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_320.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 53, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 53, 34)} \n","\n","normalization...\n","normalization done\n","1 1937\n","2 1738\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_329.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 51, 34)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 54, 31)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 51, 34)} \n","\n","normalization...\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 54, 31)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1551\n","2 1235\n","1 1455\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_297.npz\n","no resampling necessary\n","no resampling necessary\n","2 1654\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_265.npz\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 57, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 57, 37)} \n","\n","normalization...\n","normalization done\n","1 1595\n","no resampling necessary\n","2 1494\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_236.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 35)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 38)} \n","\n","normalization...\n","normalization done\n","1 2011\n","2 1643\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_308.npz\n","1 1803\n","2 1524\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_332.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 46, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 46, 33)} \n","\n","normalization...\n","normalization done\n","1 1192\n","2 1443\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_333.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 45, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 45, 32)} \n","\n","normalization...\n","normalization done\n","1 1067\n","2 1551\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_343.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 55, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 50, 35)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 51, 36)} \n","\n","normalization...\n","normalization done\n","1 1863\n","2 1633\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_356.npz\n","normalization done\n","1 1597\n","2 1801\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_370.npz\n","1 2232\n","2 1765\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_330.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 29, 55, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 29, 55, 33)} \n","\n","normalization...\n","normalization done\n","1 1777\n","2 1643\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_383.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 50, 36)} \n","\n","normalization...\n","normalization done\n","1 1751\n","2 1732\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_372.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 50, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 50, 35)} \n","\n","normalization...\n","normalization done\n","1 1519\n","2 1368\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_358.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 52, 35)} \n","\n","normalization...\n","normalization done\n","1 1561\n","2 1633\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_331.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 36, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 36, 47, 34)} \n","\n","normalization...\n","normalization done\n","1 1242\n","2 1436\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_334.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 30, 49, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 30, 49, 32)} \n","\n","normalization...\n","normalization done\n","1 1685\n","2 1295\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_345.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 48, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 48, 35)} \n","\n","normalization...\n","normalization done\n","1 2113\n","2 1284\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_385.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 47, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 47, 32)} \n","\n","normalization...\n","normalization done\n","1 1263\n","2 1269\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_335.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 49, 35)} \n","\n","normalization...\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 49, 34)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1262\n","2 1372\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_359.npz\n","1 1882\n","2 1807\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_373.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 50, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 50, 34)} \n","\n","normalization...\n","normalization done\n","1 1217\n","2 1373\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_349.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 45, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 45, 37)} \n","\n","normalization...\n","normalization done\n","1 1959\n","2 1249\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_386.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 34)} \n","\n","normalization...\n","normalization done\n","1 1298\n","2 1633\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_360.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 47, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 47, 34)} \n","\n","normalization...\n","normalization done\n","1 1298\n","2 1295\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_336.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 49, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 49, 35)} \n","\n","normalization...\n","normalization done\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 39, 48, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 39, 48, 38)} \n","\n","normalization...\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 51, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 51, 33)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1475\n","2 1467\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_350.npz\n","1 1509\n","1 2123\n","2 1571\n","2 1754\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_374.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_387.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 41, 44, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 41, 44, 33)} \n","\n","normalization...\n","normalization done\n","1 1551\n","2 1702\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_337.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 49, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 49, 34)} \n","\n","normalization...\n","normalization done\n","1 1763\n","2 1184\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_389.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 50, 36)} \n","\n","normalization...\n","normalization done\n","1 1733\n","2 1372\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_361.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 35)} \n","\n","normalization...\n","normalization done\n","1 1822\n","2 1902\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_351.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 54, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 54, 32)} \n","\n","normalization...\n","normalization done\n","1 1686\n","2 1536\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_375.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 52, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 52, 38)} \n","\n","normalization...\n","normalization done\n","1 1838\n","2 1668\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_363.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 51, 38)} \n","\n","normalization...\n","no resampling necessary\n","no resampling necessary\n","normalization done\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 43, 43, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 43, 43, 37)} \n","\n","normalization...\n","1 1966\n","normalization done\n","2 1713\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_352.npz\n","1 2007\n","2 1616\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_338.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 33, 51, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 33, 51, 38)} \n","\n","normalization...\n","normalization done\n","1 1886\n","no resampling necessary\n","no resampling necessary\n","2 1366\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_390.npz\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 55, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 55, 35)} \n","\n","normalization...\n","normalization done\n","1 1625\n","2 1956\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_376.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 47, 37)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 47, 37)} \n","\n","normalization...\n","normalization done\n","1 2482\n","2 1497\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_366.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 46, 35)} \n","\n","normalization...\n","normalization done\n","1 1592\n","2 1618\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_340.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 32)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 32)} \n","\n","normalization...\n","normalization done\n","1 1415\n","2 1340\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_353.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 31, 51, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 31, 51, 36)} \n","\n","normalization...\n","normalization done\n","1 1864\n","2 1818\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_393.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 34, 52, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 34, 52, 35)} \n","\n","normalization...\n","normalization done\n","1 1180\n","2 1577\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_378.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 50, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 50, 36)} \n","\n","normalization...\n","normalization done\n","1 1529\n","2 1383\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_354.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 35, 48, 34)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 35, 48, 34)} \n","\n","normalization...\n","normalization done\n","1 1331\n","2 1257\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_341.npz\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 57, 36)} \n","after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 42, 46, 35)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 57, 36)} \n","\n"," {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 42, 46, 35)} \n","\n","normalization...\n","normalization...\n","normalization done\n","normalization done\n","1 1730\n","1 2212\n","2 1705\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_380.npz\n","2 1939\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_367.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 32, 52, 36)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 32, 52, 36)} \n","\n","normalization...\n","normalization done\n","1 1987\n","2 1827\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_394.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 38, 47, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 38, 47, 33)} \n","\n","normalization...\n","normalization done\n","1 1819\n","2 1516\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_355.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 37, 49, 33)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 37, 49, 33)} \n","\n","normalization...\n","normalization done\n","1 1550\n","2 1602\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_381.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 40, 55, 38)} \n","after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 40, 55, 38)} \n","\n","normalization...\n","normalization done\n","1 2593\n","2 1808\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1_2D_stage0/hippocampus_368.npz\n"]}],"source":["# Prepare the Execution of nnU-Net for Dataset 4 - this is the Hippocampus Dataset here (taking 1-2 minutes)\n","!nnUNetv2_plan_and_preprocess -d 4"]},{"cell_type":"markdown","metadata":{},"source":["### 6.2.3 Training nnU-Net\n","here we will train a 3D nnU-Net on Full Resolution for 2 epochs.\n","\n","To run a normal training use: ```nnUNetTrainer``` instead of ```nnUNetTrainer_1epoch```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":476973,"status":"ok","timestamp":1643126067709,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"WSU2kFfh5GKP","outputId":"0a00d105-2174-4f07-f5c8-fff233413348"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","###############################################\n","I am running the following nnUNet: 3d_fullres\n","My trainer class is:  <class 'nnunet.training.network_training.nnUNetTrainerV2.nnUNetTrainerV2'>\n","For that I will be using the following configuration:\n","num_classes:  2\n","modalities:  {0: 'MRI'}\n","use_mask_for_norm OrderedDict([(0, False)])\n","keep_only_largest_region None\n","min_region_size_per_class None\n","min_size_per_class None\n","normalization_schemes OrderedDict([(0, 'nonCT')])\n","stages...\n","\n","stage:  0\n","{'batch_size': 9, 'num_pool_per_axis': [3, 3, 3], 'patch_size': array([40, 56, 40]), 'median_patient_size_in_voxels': array([36, 50, 35]), 'current_spacing': array([1., 1., 1.]), 'original_spacing': array([1., 1., 1.]), 'do_dummy_2D_data_aug': False, 'pool_op_kernel_sizes': [[2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}\n","\n","I am using stage 0 from these plans\n","I am using sample dice + CE loss\n","\n","I am using data from this folder:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/nnUNetData_plans_v2.1\n","###############################################\n","loading dataset\n","loading all case properties\n","2022-01-25 15:46:36.186215: Using splits from existing split file: /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task004_Hippocampus/splits_final.pkl\n","2022-01-25 15:46:36.384796: The split file contains 5 splits.\n","2022-01-25 15:46:36.388892: Desired fold for training: 0\n","2022-01-25 15:46:36.392478: This split has 208 training and 52 validation cases.\n","unpacking dataset\n","done\n","2022-01-25 15:46:46.928773: lr: 0.01\n","using pin_memory on device 0\n","using pin_memory on device 0\n","2022-01-25 15:46:55.474129: Unable to plot network architecture:\n","2022-01-25 15:46:55.560382: No module named 'hiddenlayer'\n","2022-01-25 15:46:55.644952: \n","printing the network instead:\n","\n","2022-01-25 15:46:55.705695: Generic_UNet(\n","  (conv_blocks_localization): ModuleList(\n","    (0): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (1): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (2): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","  )\n","  (conv_blocks_context): ModuleList(\n","    (0): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (1): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (2): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (3): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","  )\n","  (td): ModuleList()\n","  (tu): ModuleList(\n","    (0): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n","    (1): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n","    (2): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n","  )\n","  (seg_outputs): ModuleList(\n","    (0): Conv3d(128, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (1): Conv3d(64, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (2): Conv3d(32, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","  )\n",")\n","2022-01-25 15:46:55.794349: \n","\n","2022-01-25 15:46:55.838714: \n","epoch:  0\n","Traceback (most recent call last):\n","  File \"/usr/local/bin/nnUNet_train\", line 8, in <module>\n","    sys.exit(main())\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/run/run_training.py\", line 179, in main\n","    trainer.run_training()\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/network_training/nnUNetTrainerV2.py\", line 440, in run_training\n","    ret = super().run_training()\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/network_training/nnUNetTrainer.py\", line 317, in run_training\n","    super(nnUNetTrainer, self).run_training()\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/network_training/network_trainer.py\", line 456, in run_training\n","    l = self.run_iteration(self.tr_gen, True)\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/network_training/nnUNetTrainerV2.py\", line 249, in run_iteration\n","    l = self.loss(output, target)\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n","    return forward_call(*input, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/loss_functions/deep_supervision.py\", line 39, in forward\n","    l = weights[0] * self.loss(x[0], y[0])\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n","    return forward_call(*input, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/loss_functions/dice_loss.py\", line 348, in forward\n","    dc_loss = self.dc(net_output, target, loss_mask=mask) if self.weight_dice != 0 else 0\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n","    return forward_call(*input, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/loss_functions/dice_loss.py\", line 180, in forward\n","    tp, fp, fn, _ = get_tp_fp_fn_tn(x, y, axes, loss_mask, False)\n","  File \"/usr/local/lib/python3.7/dist-packages/nnunet/training/loss_functions/dice_loss.py\", line 129, in get_tp_fp_fn_tn\n","    y_onehot = y_onehot.cuda(net_output.device.index)\n","KeyboardInterrupt\n"]}],"source":["# train the 3d nnUnet on the Full Resolution with Dataset 4 and Cross Validation Split 0\n","!nnUNetv2_train 4 3d_fullres 0 -tr nnUNetTrainer_1epoch \n","\n","# Training for 2 iterations will take "]},{"cell_type":"markdown","metadata":{"id":"oI_Qxqlortjn"},"source":["## 6.3 Inference with Pre-Trained Models on Decathlon Prostate Dataset (WIP)\n","Here we will use a pretrained model on the Prostate Dataset and visualize the results"]},{"cell_type":"markdown","metadata":{},"source":["### 6.3.1 Downloading Pretrained Model\n","First Download the pretrained models and validate that everything works correctly "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":346105,"status":"ok","timestamp":1643126427978,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"Y-OREi8L5JBX","outputId":"96d804e1-48dc-4dae-8a90-e947e6196e4e"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","\n","######################################################\n","!!!!!!!!!!!!!!!!!!!!!!!!WARNING!!!!!!!!!!!!!!!!!!!!!!!\n","######################################################\n","Using the pretrained model weights is subject to the license of the dataset they were trained on. Some allow commercial use, others don't. It is your responsibility to make sure you use them appropriately! Use nnUNet_print_pretrained_model_info(task_name) to see a summary of the dataset and where to find its license!\n","######################################################\n","\n","Downloading pretrained model from url: https://zenodo.org/record/4485926/files/Task005_Prostate.zip?download=1\n","Download finished. Extracting...\n","Done\n"]}],"source":["# Download the Pretrained Model for the Prostate Dataset (Decathlon Dataset)\n","os.chdir(path_dict[\"nnUNet_results\"])\n","!gdown 'https://drive.google.com/uc?export=download&id=1gCdlj-O3hgoMDDw2NM8QxvFIGGVbT9Qy'\n","!nnUNetv2_install_pretrained_model_from_zip nnUNetv2_3dfullres_Model_Dataset005_Prostate.zip\n","!rm 'nnUNetv2_3dfullres_Model_Dataset005_Prostate.zip'\n","os.chdir(base_dir)\n","\n","# takes roughly 9 minutes"]},{"cell_type":"markdown","metadata":{},"source":["### 6.3.2 Convert Prostate Dataset\n","For inference the data has to be saved into the format nnU-Net expects as input (as detailed in 6.2.1)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":82642,"status":"ok","timestamp":1642875101487,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"UPOO1zhz8thI","outputId":"59e365a8-8196-4ca9-8a8f-c327f12a64d6"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n"]}],"source":["# Preprocess Prostate Dataset\n","!nnUNetv2_convert_MSD_dataset -i \"${RAW_DATA_PATH}/Task05_Prostate\""]},{"cell_type":"markdown","metadata":{},"source":["### 6.3.3 Inference on the Prostate Dataset\n","Use 3d_fullres nnUnet (-c) trained on the test set of dataset 5 (-d 5) and save the outputs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49799,"status":"ok","timestamp":1643127870499,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"IEPXgLEp_Tt0","outputId":"6161a178-b723-4eee-ff99-575f9dedb65e"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","using model stored in  /content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1\n","This model expects 2 input modalities for each image\n","Found 16 unique case ids, here are some examples: ['prostate_36' 'prostate_09' 'prostate_12' 'prostate_27' 'prostate_30'\n"," 'prostate_26' 'prostate_08' 'prostate_27' 'prostate_12' 'prostate_36']\n","If they don't look right, make sure to double check your filenames. They must end with _0000.nii.gz etc\n","number of cases: 16\n","number of cases that still need to be predicted: 0\n","emptying cuda cache\n","loading parameters for folds, None\n","folds is None so we will automatically look for output folders (not using 'all'!)\n","found the following folds:  ['/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_0', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_1', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_2', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_3', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_4']\n","2022-01-25 16:23:47.500971: Using dummy2d data augmentation\n","using the following model files:  ['/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_0/model_final_checkpoint.model', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_1/model_final_checkpoint.model', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_2/model_final_checkpoint.model', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_3/model_final_checkpoint.model', '/content/drive/My Drive/Colab Notebooks/nnUNet_Results_Folder/nnUNet/3d_fullres/Task005_Prostate/nnUNetTrainerV2__nnUNetPlansv2.1/fold_4/model_final_checkpoint.model']\n","starting preprocessing generator\n","starting prediction...\n","inference done. Now waiting for the segmentation export to finish...\n","postprocessing...\n"]}],"source":["# use fully trained nnU-Net to make predictions on data\n","!nnUNetv2_predict -i \"${nnUNet_raw}/Dataset005_Prostate/imagesTs/\" -o \"${nnUNet_results}/Dataset005_Prostate/predTs/\" -d 5 -c 3d_fullres"]},{"cell_type":"markdown","metadata":{"id":"LetWZXletANY"},"source":["## 6.3.4 Visualization of Data and Segmentations\n","Download files from Google Drive:\n","\n","Images from: ```${nnUNet_raw}/Dataset005_Prostate/imagesTs/```\n","\n","Segmentations from: ```${RESULTS_FOLDER}/Dataset005_Prostate/predTs/\"```\n","\n","\n","After downloading these files you can visualize them with any volumetric visualization program.\n","For this we would advise to use [MITK](https://www.mitk.org/wiki/The_Medical_Imaging_Interaction_Toolkit_(MITK)) which already has some great [tutorials](https://www.mitk.org/wiki/Tutorials).\n","\n","\n","Note:\n","- If you have not already downloaded it, here is the [MITK Download Link](https://www.mitk.org/wiki/Downloads)\n","\n","This is also the end of the basic tutorial."]},{"cell_type":"markdown","metadata":{"id":"Hh3rQps8uStT"},"source":["# 7. How to train and use nnU-Net on a new Dataset?\n","\n","Goal of the next Steps:\n","- How can you Implement nnU-Net for a new Dataset?\n","  - Example using the GM Spinal Cord Segmentation Challenge Dataset\n","    - [Data & Task Explanation](https://www.sciencedirect.com/science/article/pii/S1053811917302185)\n","    - [Data Download Link](http://cmictig.cs.ucl.ac.uk/niftyweb/challenge/)\n","- How to store the Data for use with nnU-Net?\n","  - General Information can be found [here](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/dataset_conversion.md)\n","- How to create the Data Fingerprint?\n","- How to create the Pipeline Fingerprint based on Rules?\n","\n","This part of the Tutorial based on: \n","\n","The [GoogleColab Notebook](https://github.com/prateekgupta891/nnUNet/blob/master/nnunetmec2020.ipynb) and the [Medium Article about nnU-Net](https://medium.com/miccai-educational-initiative/nnu-net-the-no-new-unet-for-automatic-segmentation-8d655f3f6d2a)\n","written in the context of the MICCAI Educational Initiative by:\n","- Prateek Gupta, Indian Institute of Information Technology, Pune (prateekgupta16@alumni.iiitp.ac.in)\n","- Kumar T. Rajamani, Institute of Medical Informatics, University of Lübeck, Germany (kumar.rajamani@uni-luebeck.de)\n","- Mattias P. Heinrich, Institute of Medical Informatics, University of Lübeck, Germany (heinrich@imi.uni-luebeck.de)\n","\n","Many Thanks to them!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1316,"status":"ok","timestamp":1643208247485,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"fv28nQ-ZYx8c","outputId":"37037c03-227d-4c06-8433-f01a7c6e5189"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_raw_data/Task501_SCGM created!\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_raw_data/Task501_SCGM/imagesTr created!\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_raw_data/Task501_SCGM/labelsTr created!\n","/content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_raw_data/Task501_SCGM/imagesTs created!\n"]}],"source":["# Create Folderstructure for the new dataset!\n","dataset_name = 'Dataset501_SCGM' #change here for different dataset name\n","nnunet_raw_data = os.path.join(os.getenv(\"nnUNet_raw\"))\n","task_folder_name = os.path.join(nnunet_raw_data,dataset_name)\n","train_image_dir = os.path.join(task_folder_name,'imagesTr')\n","train_label_dir = os.path.join(task_folder_name,'labelsTr')\n","test_dir = os.path.join(task_folder_name,'imagesTs')\n","\n","# Create Folder Structure for the SCGM dataset on the system\n","make_if_dont_exist(task_folder_name)\n","make_if_dont_exist(train_image_dir)\n","make_if_dont_exist(train_label_dir)\n","make_if_dont_exist(test_dir)\n","\n","training_data_name=\"training-data-gm-sc-challenge-ismrm16-v20160302b\"\n","test_data_name=\"test-data-gm-sc-challenge-ismrm16-v20160401\""]},{"cell_type":"markdown","metadata":{},"source":["## 7.1 Data Download\n","Again, now the data has to be put into the proper place in the folder structure. \n","\n","In case you are not attending an in person workshop please skip the next cell.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4632,"status":"ok","timestamp":1643208256086,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"dhjsfUOLjcgf","outputId":"83941303-f8f3-41ca-daa4-9e6ded9e4898"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading...\n","From: https://drive.google.com/uc?export=download&id=1Kynj_jFKwjXlSWkOf5A9uvfF0UAolpZl\n","To: /content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_raw_data/Task501_SCGM/training-data-gm-sc-challenge-ismrm16-v20160302b.zip\n","100% 122M/122M [00:00<00:00, 126MB/s]\n","imagesTr  labelsTr\n","imagesTs  training-data-gm-sc-challenge-ismrm16-v20160302b.zip\n"]}],"source":["# only for in poerson workshops\n","\n","os.chdir(task_folder_name)\n","# download training data\n","!gdown 'https://drive.google.com/uc?export=download&id=1gCdlj-O3hgoMDDw2NM8QxvFIGGVbT9Qy'\n","!ls\n","os.chdir(base_dir)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1643208397303,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"e7vA8XXf1DgQ","outputId":"f15723b8-f6c1-40a9-cc4f-14b54fdf48cd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training file for exists SCGM Challenge exists\n"]}],"source":["# continue from here again.\n","if os.path.isfile(os.path.join(task_folder_name, training_data_name+'.zip')) is False: \n","  print(\"Please download the dataset zipfiles and place them into the following directory: \\n {}\".format(task_folder_name))\n","else:\n","  print(f'Training file for exists SCGM Challenge exists')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1643208440013,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"PWY3RpZxafqi","outputId":"ce61140e-06be-40d9-a706-c1d87d6bdae4"},"outputs":[{"name":"stdout","output_type":"stream","text":["imagesTr  labelsTr\n","imagesTs  training-data-gm-sc-challenge-ismrm16-v20160302b.zip\n","Training file for exists SCGM Challenge exists\n","We are currently in working directory /content\n"]}],"source":["# verify that files are in the correct place!\n","os.chdir(task_folder_name)\n","!ls\n","if os.path.isfile(training_data_name+'.zip'):\n","    print(f'Training file for exists SCGM Challenge exists')\n","else:\n","    print('Training file for SCGM Challenge is not present in the directory')\n","    print(\"Please check whether {}.zip is in Folder {}\".format(training_data_name, task_folder_name))\n","\n","os.chdir(base_dir)\n","print(\"We are currently in working directory {}\".format(os.getcwd()))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6441,"status":"ok","timestamp":1643208455244,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"mG5QHzSGyLW8","outputId":"d7b54754-aa2d-43e2-fe0f-10cbc726d5da"},"outputs":[{"name":"stdout","output_type":"stream","text":["Archive:  training-data-gm-sc-challenge-ismrm16-v20160302b.zip\n","   creating: training-data-gm-sc-challenge-ismrm16-v20160302b/\n","  inflating: __MACOSX/._training-data-gm-sc-challenge-ismrm16-v20160302b  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc04-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc04-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc05-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc05-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc10-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc10-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc01-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc01-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc05-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc05-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc10-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc10-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc04-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc04-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc02-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc02-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc06-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc06-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc04-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc04-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc05-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc05-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc10-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc10-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc05-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc05-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc03-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc03-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc04-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc04-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc02-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc02-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc02-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc02-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc05-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc05-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc10-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc10-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc06-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc06-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc03-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc03-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc02-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc02-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc09-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc09-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc01-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc01-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc04-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc04-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc07-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc07-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc02-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc02-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc03-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc03-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc03-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc03-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc08-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc08-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc02-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc02-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc03-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc03-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc10-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc10-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc05-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc05-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc07-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc07-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc02-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc02-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc10-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc10-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc03-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc03-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc07-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc07-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc03-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc03-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc02-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc02-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc04-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc04-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc02-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc02-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc02-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc02-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc04-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc04-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc10-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc10-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc05-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc05-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc10-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc10-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc05-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc05-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc01-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc01-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc04-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc04-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc03-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc03-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc10-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc10-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc05-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc05-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc04-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc04-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc07-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc07-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc07-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc07-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc07-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc07-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc06-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc06-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc08-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc08-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc07-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc07-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc09-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc09-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc06-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc06-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc08-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc08-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc07-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc07-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc06-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc06-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc01-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc01-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc06-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc06-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc05-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc05-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc09-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc09-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc06-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc06-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc07-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc07-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc08-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc08-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc09-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc09-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc01-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc01-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc01-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc01-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc09-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc09-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc08-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc08-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc06-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc06-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc01-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc01-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc01-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc01-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc08-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc08-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc07-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc07-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc01-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc01-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc07-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc07-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc09-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc09-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc08-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc08-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc09-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc09-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc08-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc08-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc01-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc01-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc01-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc01-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc05-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc05-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc08-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc08-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc09-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc09-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc06-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc06-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc07-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc07-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc06-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc06-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc09-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc09-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc02-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc02-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc06-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc06-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc07-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc07-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc01-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc01-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc06-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc06-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc08-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc08-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc01-mask-r1.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc01-mask-r1.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc03-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc03-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc08-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc08-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc07-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc07-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc06-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc06-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc07-mask-r3.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc07-mask-r3.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc02-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc02-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc03-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc03-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc06-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc06-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc07-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc07-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc06-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc06-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc09-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc09-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc10-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc10-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc01-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc01-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc02-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc02-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc09-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc09-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc03-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc03-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc05-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc05-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc10-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc10-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc06-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc06-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc03-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc03-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc04-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc04-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc05-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc05-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc10-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc10-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc09-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc09-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc04-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc04-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc10-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc10-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc01-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc01-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc05-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc05-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc08-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc08-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc09-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc09-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc05-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc05-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc10-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc10-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc01-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc01-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc06-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc06-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc09-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc09-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc02-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc02-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc01-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc01-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc02-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc02-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc08-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc08-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc07-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc07-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc08-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc08-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc04-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc04-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc09-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc09-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc05-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc05-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc03-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc03-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc04-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc04-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc08-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc08-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc10-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc10-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc05-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc05-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc01-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc01-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc04-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc04-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc03-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc03-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc03-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc03-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc08-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc08-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc08-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc08-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc04-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc04-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc10-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc10-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc07-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc07-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc03-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc03-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc04-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc04-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc05-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc05-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc02-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc02-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc02-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc02-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc09-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc09-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc03-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc03-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc08-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc08-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc06-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc06-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc07-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc07-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc06-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc06-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc10-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc10-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc08-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc08-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc06-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc06-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc09-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc09-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc05-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc05-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc10-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc10-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc01-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc01-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc08-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc08-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc02-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc02-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc01-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc01-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc05-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc05-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc10-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc10-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc09-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc09-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc04-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc04-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc02-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc02-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc04-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc04-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc09-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc09-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc05-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc05-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc01-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc01-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc03-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc03-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc02-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc02-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc01-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc01-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc10-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc10-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc06-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc06-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc05-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc05-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc10-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc10-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/license.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._license.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc05-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc05-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc02-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc02-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc03-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc03-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc02-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc02-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc02-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc02-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc10-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc10-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc09-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc09-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc06-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc06-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc07-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc07-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc09-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc09-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc09-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc09-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc04-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc04-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc03-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc03-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc02-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc02-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc08-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc08-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc03-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc03-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc10-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc10-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc06-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc06-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc07-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc07-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc08-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc08-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc06-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc06-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc09-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc09-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc03-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc03-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc07-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc07-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc04-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc04-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc07-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc07-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc03-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc03-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc04-image.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc04-image.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc10-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc10-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc05-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc05-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc04-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc04-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc08-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc08-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc04-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc04-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc01-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc01-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site4-sc09-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site4-sc09-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site2-sc08-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site2-sc08-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc04-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc04-mask-r4.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc07-mask-r2.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc07-mask-r2.nii.gz  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site3-sc01-levels.txt  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site3-sc01-levels.txt  \n","  inflating: training-data-gm-sc-challenge-ismrm16-v20160302b/site1-sc03-mask-r4.nii.gz  \n","  inflating: __MACOSX/training-data-gm-sc-challenge-ismrm16-v20160302b/._site1-sc03-mask-r4.nii.gz  \n"]}],"source":["#unzipping in nnUNet_raw folder the training data\n","os.chdir(task_folder_name)\n","!unzip -o training-data-gm-sc-challenge-ismrm16-v20160302b.zip\n","!rm training-data-gm-sc-challenge-ismrm16-v20160302b.zip\n","os.chdir(base_dir)"]},{"cell_type":"markdown","metadata":{"id":"hKs8v1ndynNJ"},"source":["## 7.2 Data Preprocessing\n","We have 4 annotation of the same image, by different experts in the SCGM Challenge. ( Image , Ann1 ) and ( Image , Ann2 ) can be considered as a different image and label pairs. Hence, 4 copies of the training .nii.gz file is created with its mapping to the respective label name.\n","\n","For this the data is renamed and relocated"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oawSNmUjya-u"},"outputs":[],"source":["# function for copying, savind and renaming\n","def copy_and_rename(old_location,old_file_name,new_location,new_filename,delete_original = False):\n","\n","    shutil.copy(os.path.join(old_location,old_file_name),new_location)\n","    os.rename(os.path.join(new_location,old_file_name),os.path.join(new_location,new_filename))\n","    if delete_original:\n","        os.remove(os.path.join(old_location,old_file_name))\n","\n","# putting training images into folder\n","\n","mask_count = 4 # change if more mask is available\n","base_data_folder_name = os.path.join(task_folder_name, \"training-data-gm-sc-challenge-ismrm16-v20160302b\")\n","\n","for file in os.listdir(base_data_folder_name):\n","    # print(file)\n","    if file.endswith('.nii.gz'):\n","        if file.find('mask')!=-1:\n","            # putting mask\n","            shutil.move(os.path.join(base_data_folder_name,file),train_label_dir)\n","        else:\n","            # making 4 copies\n","            for mask in range(1,mask_count+1):\n","                new_filename = file[:file.find('-image')] + '-mask-r' + str(mask) + '.nii.gz'\n","                if mask==mask_count:\n","                    copy_and_rename(base_data_folder_name,file,train_image_dir,new_filename,delete_original = True)\n","                else:\n","                    copy_and_rename(base_data_folder_name,file,train_image_dir,new_filename)\n","    # removing all other files installed due to the unzip\n","    elif file.endswith('.txt'):\n","        os.remove(os.path.join(base_data_folder_name,file))"]},{"cell_type":"markdown","metadata":{"id":"uOkK_ZcJ_zuu"},"source":["## 7.3 Verification of Data\n","\n","Before going any further, verify that the data is present and labels and data matches."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":385,"status":"ok","timestamp":1643208476256,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"NiNcsMqX_9Nu","outputId":"b4287bc5-a18f-4562-f9d3-0486eba9dc34"},"outputs":[{"name":"stdout","output_type":"stream","text":["train image files: 160\n","train label files: 160\n","Matches: 160\n"]}],"source":["train_files = os.listdir(train_image_dir)\n","label_files = os.listdir(train_label_dir)\n","print(\"train image files:\",len(train_files))\n","print(\"train label files:\",len(label_files))\n","print(\"Matches:\",len(set(train_files).intersection(set(label_files))))\n","\n","assert len(set(train_files).intersection(set(label_files))) == 160 #should be equal to 160 for SCGM Challenge"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2438,"status":"ok","timestamp":1643208484172,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"NaTA4qO2_lKP","outputId":"1b98dc7b-0295-4a45-e0b9-28f4d88f2d32"},"outputs":[{"name":"stdout","output_type":"stream","text":["Renamed to site4-sc01-mask-r4_0000.nii.gz\n","Renamed to site4-sc01-mask-r1_0000.nii.gz\n","Renamed to site4-sc01-mask-r2_0000.nii.gz\n","Renamed to site4-sc01-mask-r3_0000.nii.gz\n","Renamed to site4-sc06-mask-r4_0000.nii.gz\n","Renamed to site4-sc06-mask-r1_0000.nii.gz\n","Renamed to site4-sc06-mask-r2_0000.nii.gz\n","Renamed to site4-sc06-mask-r3_0000.nii.gz\n","Renamed to site1-sc06-mask-r4_0000.nii.gz\n","Renamed to site1-sc06-mask-r1_0000.nii.gz\n","Renamed to site1-sc06-mask-r2_0000.nii.gz\n","Renamed to site1-sc06-mask-r3_0000.nii.gz\n","Renamed to site1-sc01-mask-r4_0000.nii.gz\n","Renamed to site1-sc01-mask-r1_0000.nii.gz\n","Renamed to site1-sc01-mask-r2_0000.nii.gz\n","Renamed to site1-sc01-mask-r3_0000.nii.gz\n","Renamed to site4-sc07-mask-r4_0000.nii.gz\n","Renamed to site4-sc07-mask-r1_0000.nii.gz\n","Renamed to site4-sc07-mask-r2_0000.nii.gz\n","Renamed to site4-sc07-mask-r3_0000.nii.gz\n","Renamed to site1-sc07-mask-r4_0000.nii.gz\n","Renamed to site1-sc07-mask-r1_0000.nii.gz\n","Renamed to site1-sc07-mask-r2_0000.nii.gz\n","Renamed to site1-sc07-mask-r3_0000.nii.gz\n","Renamed to site3-sc06-mask-r4_0000.nii.gz\n","Renamed to site3-sc06-mask-r1_0000.nii.gz\n","Renamed to site3-sc06-mask-r2_0000.nii.gz\n","Renamed to site3-sc06-mask-r3_0000.nii.gz\n","Renamed to site3-sc01-mask-r4_0000.nii.gz\n","Renamed to site3-sc01-mask-r1_0000.nii.gz\n","Renamed to site3-sc01-mask-r2_0000.nii.gz\n","Renamed to site3-sc01-mask-r3_0000.nii.gz\n","Renamed to site2-sc07-mask-r4_0000.nii.gz\n","Renamed to site2-sc07-mask-r1_0000.nii.gz\n","Renamed to site2-sc07-mask-r2_0000.nii.gz\n","Renamed to site2-sc07-mask-r3_0000.nii.gz\n","Renamed to site2-sc06-mask-r4_0000.nii.gz\n","Renamed to site2-sc06-mask-r1_0000.nii.gz\n","Renamed to site2-sc06-mask-r2_0000.nii.gz\n","Renamed to site2-sc06-mask-r3_0000.nii.gz\n","Renamed to site2-sc01-mask-r4_0000.nii.gz\n","Renamed to site2-sc01-mask-r1_0000.nii.gz\n","Renamed to site2-sc01-mask-r2_0000.nii.gz\n","Renamed to site2-sc01-mask-r3_0000.nii.gz\n","Renamed to site3-sc07-mask-r4_0000.nii.gz\n","Renamed to site3-sc07-mask-r1_0000.nii.gz\n","Renamed to site3-sc07-mask-r2_0000.nii.gz\n","Renamed to site3-sc07-mask-r3_0000.nii.gz\n","Renamed to site1-sc09-mask-r4_0000.nii.gz\n","Renamed to site1-sc09-mask-r1_0000.nii.gz\n","Renamed to site1-sc09-mask-r2_0000.nii.gz\n","Renamed to site1-sc09-mask-r3_0000.nii.gz\n","Renamed to site2-sc03-mask-r4_0000.nii.gz\n","Renamed to site2-sc03-mask-r1_0000.nii.gz\n","Renamed to site2-sc03-mask-r2_0000.nii.gz\n","Renamed to site2-sc03-mask-r3_0000.nii.gz\n","Renamed to site2-sc04-mask-r4_0000.nii.gz\n","Renamed to site2-sc04-mask-r1_0000.nii.gz\n","Renamed to site2-sc04-mask-r2_0000.nii.gz\n","Renamed to site2-sc04-mask-r3_0000.nii.gz\n","Renamed to site3-sc10-mask-r4_0000.nii.gz\n","Renamed to site3-sc10-mask-r1_0000.nii.gz\n","Renamed to site3-sc10-mask-r2_0000.nii.gz\n","Renamed to site3-sc10-mask-r3_0000.nii.gz\n","Renamed to site3-sc05-mask-r4_0000.nii.gz\n","Renamed to site3-sc05-mask-r1_0000.nii.gz\n","Renamed to site3-sc05-mask-r2_0000.nii.gz\n","Renamed to site3-sc05-mask-r3_0000.nii.gz\n","Renamed to site4-sc09-mask-r4_0000.nii.gz\n","Renamed to site4-sc09-mask-r1_0000.nii.gz\n","Renamed to site4-sc09-mask-r2_0000.nii.gz\n","Renamed to site4-sc09-mask-r3_0000.nii.gz\n","Renamed to site3-sc02-mask-r4_0000.nii.gz\n","Renamed to site3-sc02-mask-r1_0000.nii.gz\n","Renamed to site3-sc02-mask-r2_0000.nii.gz\n","Renamed to site3-sc02-mask-r3_0000.nii.gz\n","Renamed to site1-sc08-mask-r4_0000.nii.gz\n","Renamed to site1-sc08-mask-r1_0000.nii.gz\n","Renamed to site1-sc08-mask-r2_0000.nii.gz\n","Renamed to site1-sc08-mask-r3_0000.nii.gz\n","Renamed to site3-sc03-mask-r4_0000.nii.gz\n","Renamed to site3-sc03-mask-r1_0000.nii.gz\n","Renamed to site3-sc03-mask-r2_0000.nii.gz\n","Renamed to site3-sc03-mask-r3_0000.nii.gz\n","Renamed to site4-sc08-mask-r4_0000.nii.gz\n","Renamed to site4-sc08-mask-r1_0000.nii.gz\n","Renamed to site4-sc08-mask-r2_0000.nii.gz\n","Renamed to site4-sc08-mask-r3_0000.nii.gz\n","Renamed to site3-sc04-mask-r4_0000.nii.gz\n","Renamed to site3-sc04-mask-r1_0000.nii.gz\n","Renamed to site3-sc04-mask-r2_0000.nii.gz\n","Renamed to site3-sc04-mask-r3_0000.nii.gz\n","Renamed to site2-sc10-mask-r4_0000.nii.gz\n","Renamed to site2-sc10-mask-r1_0000.nii.gz\n","Renamed to site2-sc10-mask-r2_0000.nii.gz\n","Renamed to site2-sc10-mask-r3_0000.nii.gz\n","Renamed to site2-sc05-mask-r4_0000.nii.gz\n","Renamed to site2-sc05-mask-r1_0000.nii.gz\n","Renamed to site2-sc05-mask-r2_0000.nii.gz\n","Renamed to site2-sc05-mask-r3_0000.nii.gz\n","Renamed to site2-sc02-mask-r4_0000.nii.gz\n","Renamed to site2-sc02-mask-r1_0000.nii.gz\n","Renamed to site2-sc02-mask-r2_0000.nii.gz\n","Renamed to site2-sc02-mask-r3_0000.nii.gz\n","Renamed to site4-sc10-mask-r4_0000.nii.gz\n","Renamed to site4-sc10-mask-r1_0000.nii.gz\n","Renamed to site4-sc10-mask-r2_0000.nii.gz\n","Renamed to site4-sc10-mask-r3_0000.nii.gz\n","Renamed to site2-sc08-mask-r4_0000.nii.gz\n","Renamed to site2-sc08-mask-r1_0000.nii.gz\n","Renamed to site2-sc08-mask-r2_0000.nii.gz\n","Renamed to site2-sc08-mask-r3_0000.nii.gz\n","Renamed to site4-sc02-mask-r4_0000.nii.gz\n","Renamed to site4-sc02-mask-r1_0000.nii.gz\n","Renamed to site4-sc02-mask-r2_0000.nii.gz\n","Renamed to site4-sc02-mask-r3_0000.nii.gz\n","Renamed to site3-sc09-mask-r4_0000.nii.gz\n","Renamed to site3-sc09-mask-r1_0000.nii.gz\n","Renamed to site3-sc09-mask-r2_0000.nii.gz\n","Renamed to site3-sc09-mask-r3_0000.nii.gz\n","Renamed to site4-sc05-mask-r4_0000.nii.gz\n","Renamed to site4-sc05-mask-r1_0000.nii.gz\n","Renamed to site4-sc05-mask-r2_0000.nii.gz\n","Renamed to site4-sc05-mask-r3_0000.nii.gz\n","Renamed to site1-sc10-mask-r4_0000.nii.gz\n","Renamed to site1-sc10-mask-r1_0000.nii.gz\n","Renamed to site1-sc10-mask-r2_0000.nii.gz\n","Renamed to site1-sc10-mask-r3_0000.nii.gz\n","Renamed to site1-sc05-mask-r4_0000.nii.gz\n","Renamed to site1-sc05-mask-r1_0000.nii.gz\n","Renamed to site1-sc05-mask-r2_0000.nii.gz\n","Renamed to site1-sc05-mask-r3_0000.nii.gz\n","Renamed to site1-sc02-mask-r4_0000.nii.gz\n","Renamed to site1-sc02-mask-r1_0000.nii.gz\n","Renamed to site1-sc02-mask-r2_0000.nii.gz\n","Renamed to site1-sc02-mask-r3_0000.nii.gz\n","Renamed to site4-sc04-mask-r4_0000.nii.gz\n","Renamed to site4-sc04-mask-r1_0000.nii.gz\n","Renamed to site4-sc04-mask-r2_0000.nii.gz\n","Renamed to site4-sc04-mask-r3_0000.nii.gz\n","Renamed to site3-sc08-mask-r4_0000.nii.gz\n","Renamed to site3-sc08-mask-r1_0000.nii.gz\n","Renamed to site3-sc08-mask-r2_0000.nii.gz\n","Renamed to site3-sc08-mask-r3_0000.nii.gz\n","Renamed to site4-sc03-mask-r4_0000.nii.gz\n","Renamed to site4-sc03-mask-r1_0000.nii.gz\n","Renamed to site4-sc03-mask-r2_0000.nii.gz\n","Renamed to site4-sc03-mask-r3_0000.nii.gz\n","Renamed to site2-sc09-mask-r4_0000.nii.gz\n","Renamed to site2-sc09-mask-r1_0000.nii.gz\n","Renamed to site2-sc09-mask-r2_0000.nii.gz\n","Renamed to site2-sc09-mask-r3_0000.nii.gz\n","Renamed to site1-sc03-mask-r4_0000.nii.gz\n","Renamed to site1-sc03-mask-r1_0000.nii.gz\n","Renamed to site1-sc03-mask-r2_0000.nii.gz\n","Renamed to site1-sc03-mask-r3_0000.nii.gz\n","Renamed to site1-sc04-mask-r4_0000.nii.gz\n","Renamed to site1-sc04-mask-r1_0000.nii.gz\n","Renamed to site1-sc04-mask-r2_0000.nii.gz\n","Renamed to site1-sc04-mask-r3_0000.nii.gz\n"]}],"source":["#renaming to add the modality for SCGM there is only one modality \n","#images should be added with 0000\n","#can be skipped if modality is already mentioned\n","#re-write for multiple modalities\n","\n","def check_modality(filename):\n","    \"\"\"\n","    check for the existence of modality\n","    return False if modality is not found else True\n","    \"\"\"\n","    end = filename.find('.nii.gz')\n","    modality = filename[end-4:end]\n","    for mod in modality: \n","        if not(ord(mod)>=48 and ord(mod)<=57): #if not in 0 to 9 digits\n","            return False\n","    return True\n","\n","def rename_for_single_modality(directory):\n","    \n","    for file in os.listdir(directory):\n","        \n","        if check_modality(file)==False:\n","            new_name = file[:file.find('.nii.gz')]+\"_0000.nii.gz\"\n","            os.rename(os.path.join(directory,file),os.path.join(directory,new_name))\n","            print(f\"Renamed to {new_name}\")\n","        else:\n","            print(f\"Modality present: {file}\")\n","\n","rename_for_single_modality(train_image_dir)\n","\n","# again skip test due to non available data\n","# rename_for_single_modality(test_dir)"]},{"cell_type":"markdown","metadata":{"id":"l42eWsw7zKAN"},"source":["## 7.4 Creation of the Dataset501 for the SCGMC Dataset\n","The Dataset gives nnU-Net Information for the Planning and Preprocessing Step.\n","\n","Based on the parameters of the dataset, the \"optimal strategy\" is selected (data fingerprint & pipeline fingerprint)\n","\n","This is done by creating the ```dataset.json``` file.\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":392,"status":"ok","timestamp":1643208513681,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"kVCs1WSCywpH","outputId":"327ea928-ab3d-465d-a99e-7f18259f1a4e"},"outputs":[{"name":"stdout","output_type":"stream","text":["dataset.json created!\n"]}],"source":["overwrite_json_file = True #make it True if you want to overwrite the dataset.json file in Dataset_folder\n","json_file_exist = False\n","\n","if os.path.exists(os.path.join(task_folder_name,'dataset.json')):\n","    print('dataset.json already exist!')\n","    json_file_exist = True\n","\n","if json_file_exist==False or overwrite_json_file:\n","\n","    json_dict = OrderedDict()\n","    json_dict['name'] = dataset_name\n","    json_dict['description'] = \"Spinal Cord Grey Matter Segmenation Challenge\"\n","    json_dict['tensorImageSize'] = \"3D\"\n","    json_dict['reference'] = \"see challenge website\"\n","    json_dict['licence'] = \"see challenge website\"\n","    json_dict['release'] = \"0.0\"\n","\n","    #you may mention more than one modality\n","    json_dict['channel_names'] = {\n","        \"0\": \"MRI\"\n","    }\n","\n","    # set expected file ending\n","    json_dict[\"file_ending\"] = \".nii.gz\"\n","\n","    #label names should be mentioned for all the labels in the dataset\n","    json_dict['labels'] = {\n","        \"background\": 0,\n","        \"grey matter\": 1,\n","        \"white matter\": 2\n","    }\n","    \n","    train_ids = os.listdir(train_label_dir)\n","    test_ids = os.listdir(test_dir)\n","    json_dict['numTraining'] = len(train_ids)\n","    json_dict['numTest'] = len(test_ids)\n","\n","\n","    with open(os.path.join(task_folder_name,\"dataset.json\"), 'w') as f:\n","        json.dump(json_dict, f, indent=4, sort_keys=True)\n","\n","    if os.path.exists(os.path.join(task_folder_name,'dataset.json')):\n","        if json_file_exist==False:\n","            print('dataset.json created!')\n","        else: \n","            print('dataset.json overwritten!')"]},{"cell_type":"markdown","metadata":{"id":"R-kguRqvAeb6"},"source":["## 7.5 Dataset Preprocessing\n","Also to ensure that nnU-Net can be trained on the dataset a integrity check is performed."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":903679,"status":"ok","timestamp":1643209422073,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"z5RYzcjxAVhk","outputId":"f6cf0430-ea6d-4154-951a-15d44c350629"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","Verifying training set\n","checking case site2-sc07-mask-r1\n","checking case site4-sc01-mask-r4\n","checking case site3-sc07-mask-r2\n","checking case site3-sc10-mask-r1\n","checking case site2-sc01-mask-r4\n","checking case site4-sc01-mask-r3\n","checking case site3-sc02-mask-r1\n","checking case site3-sc09-mask-r2\n","checking case site1-sc03-mask-r2\n","checking case site2-sc06-mask-r1\n","checking case site1-sc04-mask-r4\n","checking case site2-sc05-mask-r1\n","checking case site1-sc06-mask-r4\n","checking case site4-sc01-mask-r2\n","checking case site3-sc03-mask-r3\n","checking case site4-sc05-mask-r3\n","checking case site1-sc02-mask-r1\n","checking case site1-sc09-mask-r4\n","checking case site3-sc08-mask-r1\n","checking case site3-sc06-mask-r1\n","checking case site1-sc01-mask-r3\n","checking case site2-sc05-mask-r3\n","checking case site4-sc08-mask-r4\n","checking case site4-sc03-mask-r3\n","checking case site3-sc03-mask-r1\n","checking case site1-sc06-mask-r2\n","checking case site2-sc10-mask-r4\n","checking case site1-sc03-mask-r4\n","checking case site2-sc07-mask-r4\n","checking case site2-sc06-mask-r4\n","checking case site1-sc08-mask-r4\n","checking case site4-sc05-mask-r1\n","checking case site3-sc06-mask-r4\n","checking case site1-sc07-mask-r1\n","checking case site3-sc01-mask-r1\n","checking case site2-sc04-mask-r4\n","checking case site3-sc10-mask-r3\n","checking case site4-sc07-mask-r1\n","checking case site2-sc06-mask-r2\n","checking case site2-sc01-mask-r3\n","checking case site4-sc06-mask-r2\n","checking case site4-sc03-mask-r4\n","checking case site4-sc07-mask-r3\n","checking case site3-sc10-mask-r4\n","checking case site2-sc10-mask-r1\n","checking case site4-sc06-mask-r1\n","checking case site4-sc07-mask-r2\n","checking case site4-sc02-mask-r3\n","checking case site3-sc02-mask-r3\n","checking case site4-sc08-mask-r3\n","checking case site2-sc01-mask-r1\n","checking case site1-sc07-mask-r4\n","checking case site3-sc01-mask-r3\n","checking case site2-sc06-mask-r3\n","checking case site1-sc06-mask-r1\n","checking case site2-sc02-mask-r1\n","checking case site1-sc09-mask-r3\n","checking case site2-sc08-mask-r1\n","checking case site4-sc03-mask-r1\n","checking case site1-sc10-mask-r2\n","checking case site2-sc03-mask-r1\n","checking case site2-sc09-mask-r2\n","checking case site3-sc06-mask-r2\n","checking case site2-sc10-mask-r3\n","checking case site4-sc06-mask-r3\n","checking case site2-sc02-mask-r2\n","checking case site1-sc02-mask-r2\n","checking case site4-sc04-mask-r2\n","checking case site3-sc01-mask-r4\n","checking case site4-sc02-mask-r2\n","checking case site3-sc02-mask-r2\n","checking case site4-sc07-mask-r4\n","checking case site1-sc04-mask-r2\n","checking case site1-sc08-mask-r2\n","checking case site4-sc06-mask-r4\n","checking case site1-sc05-mask-r2\n","checking case site1-sc01-mask-r2\n","checking case site2-sc03-mask-r4\n","checking case site4-sc04-mask-r3\n","checking case site3-sc04-mask-r1\n","checking case site2-sc09-mask-r1\n","checking case site2-sc04-mask-r1\n","checking case site3-sc09-mask-r1\n","checking case site2-sc07-mask-r2\n","checking case site4-sc09-mask-r3\n","checking case site1-sc04-mask-r3\n","checking case site1-sc05-mask-r4\n","checking case site3-sc06-mask-r3\n","checking case site3-sc09-mask-r4\n","checking case site1-sc01-mask-r4\n","checking case site3-sc04-mask-r4\n","checking case site1-sc08-mask-r1\n","checking case site1-sc01-mask-r1\n","checking case site1-sc09-mask-r2\n","checking case site3-sc07-mask-r4\n","checking case site2-sc04-mask-r2\n","checking case site3-sc07-mask-r3\n","checking case site4-sc05-mask-r2\n","checking case site4-sc10-mask-r1\n","checking case site3-sc04-mask-r3\n","checking case site2-sc09-mask-r3\n","checking case site4-sc04-mask-r1\n","checking case site3-sc02-mask-r4\n","checking case site3-sc08-mask-r3\n","checking case site4-sc08-mask-r2\n","checking case site3-sc08-mask-r4\n","checking case site1-sc09-mask-r1\n","checking case site3-sc04-mask-r2\n","checking case site4-sc04-mask-r4\n","checking case site3-sc07-mask-r1\n","checking case site3-sc09-mask-r3\n","checking case site1-sc10-mask-r1\n","checking case site3-sc10-mask-r2\n","checking case site3-sc05-mask-r1\n","checking case site1-sc06-mask-r3\n","checking case site3-sc03-mask-r4\n","checking case site2-sc08-mask-r3\n","checking case site4-sc09-mask-r4\n","checking case site2-sc10-mask-r2\n","checking case site1-sc05-mask-r3\n","checking case site1-sc07-mask-r3\n","checking case site2-sc09-mask-r4\n","checking case site1-sc03-mask-r1\n","checking case site2-sc08-mask-r4\n","checking case site2-sc05-mask-r4\n","checking case site3-sc05-mask-r4\n","checking case site1-sc10-mask-r3\n","checking case site3-sc05-mask-r2\n","checking case site4-sc10-mask-r2\n","checking case site4-sc01-mask-r1\n","checking case site3-sc05-mask-r3\n","checking case site1-sc05-mask-r1\n","checking case site4-sc09-mask-r1\n","checking case site2-sc02-mask-r4\n","checking case site1-sc10-mask-r4\n","checking case site4-sc05-mask-r4\n","checking case site2-sc01-mask-r2\n","checking case site4-sc02-mask-r1\n","checking case site2-sc03-mask-r3\n","checking case site4-sc08-mask-r1\n","checking case site2-sc04-mask-r3\n","checking case site1-sc04-mask-r1\n","checking case site1-sc07-mask-r2\n","checking case site4-sc10-mask-r4\n","checking case site1-sc03-mask-r3\n","checking case site2-sc02-mask-r3\n","checking case site1-sc02-mask-r3\n","checking case site3-sc03-mask-r2\n","checking case site4-sc02-mask-r4\n","checking case site2-sc07-mask-r3\n","checking case site4-sc03-mask-r2\n","checking case site1-sc08-mask-r3\n","checking case site1-sc02-mask-r4\n","checking case site3-sc01-mask-r2\n","checking case site4-sc09-mask-r2\n","checking case site3-sc08-mask-r2\n","checking case site2-sc03-mask-r2\n","checking case site4-sc10-mask-r3\n","checking case site2-sc08-mask-r2\n","checking case site2-sc05-mask-r2\n","Verifying label values\n","Expected label values are [0, 1, 2]\n","Labels OK\n","WARNING: Not all images in the dataset have the same axis ordering. We very strongly recommend you correct that by reorienting the data. fslreorient2std should do the trick\n","Dataset OK\n","site2-sc07-mask-r1\n","site4-sc01-mask-r3\n","site4-sc01-mask-r4\n","site3-sc02-mask-r1\n","site3-sc07-mask-r2\n","site3-sc09-mask-r2\n","site1-sc04-mask-r4\n","site3-sc10-mask-r1\n","site1-sc03-mask-r2\n","site2-sc05-mask-r1\n","site2-sc01-mask-r4\n","site2-sc06-mask-r1\n","site1-sc06-mask-r4\n","site4-sc05-mask-r3\n","site1-sc01-mask-r3\n","site1-sc06-mask-r2\n","site1-sc08-mask-r4\n","site2-sc04-mask-r4\n","site4-sc06-mask-r2\n","site4-sc01-mask-r2\n","site4-sc06-mask-r1\n","site2-sc05-mask-r3\n","site3-sc10-mask-r3\n","site1-sc02-mask-r1\n","site4-sc03-mask-r4\n","site4-sc05-mask-r1\n","site3-sc03-mask-r3\n","site2-sc10-mask-r4\n","site4-sc07-mask-r2\n","site4-sc08-mask-r4\n","site4-sc07-mask-r1\n","site1-sc09-mask-r4\n","site4-sc07-mask-r3\n","site3-sc06-mask-r4\n","site2-sc01-mask-r1\n","site1-sc03-mask-r4\n","site4-sc02-mask-r3\n","site3-sc08-mask-r1\n","site4-sc03-mask-r3\n","site2-sc06-mask-r2\n","site3-sc10-mask-r4\n","site1-sc07-mask-r1\n","site1-sc07-mask-r4\n","site2-sc07-mask-r4\n","site3-sc02-mask-r3\n","site3-sc06-mask-r1\n","site3-sc03-mask-r1\n","site2-sc01-mask-r3\n","site2-sc10-mask-r1\n","site3-sc01-mask-r1\n","site3-sc01-mask-r3\n","site2-sc06-mask-r4\n","site4-sc08-mask-r3\n","site2-sc02-mask-r1\n","site2-sc03-mask-r1\n","site2-sc02-mask-r2\n","site2-sc06-mask-r3\n","site3-sc02-mask-r2\n","site1-sc05-mask-r2\n","site2-sc09-mask-r1\n","site1-sc04-mask-r3\n","site1-sc06-mask-r1\n","site1-sc09-mask-r3\n","site4-sc07-mask-r4\n","site1-sc01-mask-r2\n","site2-sc09-mask-r2\n","site2-sc04-mask-r1\n","site1-sc02-mask-r2\n","site1-sc05-mask-r4\n","site3-sc04-mask-r4\n","site2-sc08-mask-r1\n","site1-sc04-mask-r2\n","site2-sc03-mask-r4\n","site3-sc06-mask-r2\n","site3-sc09-mask-r1\n","site4-sc04-mask-r2\n","site3-sc06-mask-r3\n","site1-sc08-mask-r1\n","site4-sc03-mask-r1\n","site1-sc08-mask-r2\n","site4-sc04-mask-r3\n","site2-sc10-mask-r3\n","site2-sc07-mask-r2\n","site3-sc01-mask-r4\n","site3-sc09-mask-r4\n","site1-sc01-mask-r1\n","site1-sc10-mask-r2\n","site4-sc06-mask-r4\n","site3-sc04-mask-r1\n","site4-sc06-mask-r3\n","site4-sc09-mask-r3\n","site4-sc02-mask-r2\n","site1-sc01-mask-r4\n","site1-sc09-mask-r2\n","site2-sc04-mask-r2\n","site2-sc09-mask-r3\n","site3-sc08-mask-r4\n","site3-sc09-mask-r3\n","site3-sc03-mask-r4\n","site1-sc07-mask-r3\n","site3-sc05-mask-r4\n","site3-sc07-mask-r4\n","site3-sc07-mask-r3\n","site2-sc08-mask-r3\n","site1-sc10-mask-r1\n","site4-sc04-mask-r1\n","site2-sc09-mask-r4\n","site1-sc09-mask-r1\n","site1-sc10-mask-r3\n","site3-sc05-mask-r3\n","site4-sc05-mask-r2\n","site4-sc09-mask-r4\n","site3-sc10-mask-r2\n","site3-sc02-mask-r4\n","site1-sc03-mask-r1\n","site3-sc04-mask-r2\n","site3-sc05-mask-r2\n","site1-sc05-mask-r1\n","site4-sc10-mask-r1\n","site2-sc10-mask-r2\n","site3-sc05-mask-r1\n","site3-sc08-mask-r3\n","site2-sc08-mask-r4\n","site4-sc04-mask-r4\n","site4-sc10-mask-r2\n","site4-sc09-mask-r1\n","site3-sc04-mask-r3\n","site1-sc05-mask-r3\n","site1-sc06-mask-r3\n","site4-sc08-mask-r2\n","site2-sc05-mask-r4\n","site3-sc07-mask-r1\n","site4-sc01-mask-r1\n","site2-sc02-mask-r4\n","site4-sc05-mask-r4\n","site2-sc04-mask-r3\n","site2-sc02-mask-r3\n","site4-sc03-mask-r2\n","site3-sc08-mask-r2\n","site1-sc10-mask-r4\n","site1-sc04-mask-r1\n","site2-sc01-mask-r2\n","site1-sc02-mask-r3\n","site1-sc08-mask-r3\n","site2-sc03-mask-r2\n","site1-sc07-mask-r2\n","site4-sc02-mask-r1\n","site3-sc03-mask-r2\n","site4-sc10-mask-r3\n","site1-sc02-mask-r4\n","site4-sc10-mask-r4\n","site2-sc03-mask-r3\n","site4-sc02-mask-r4\n","site2-sc08-mask-r2\n","site3-sc01-mask-r2\n","site1-sc03-mask-r3\n","site4-sc08-mask-r1\n","site2-sc07-mask-r3\n","site2-sc05-mask-r2\n","site4-sc09-mask-r2\n","\n","\n","\n"," Task101_SCGM\n","number of threads:  (8, 8) \n","\n","not using nonzero mask for normalization\n","Are we using the nonzero mask for normalizaion? OrderedDict([(0, False)])\n","the median shape of the dataset is  [ 20.50000067 401.02463054 401.02463054]\n","the max shape in the dataset is  [ 41.99999199 485.51724138 410.48275862]\n","the min shape in the dataset is  [  5.99999657 126.10837438 126.10837438]\n","we don't want feature maps smaller than  4  in the bottleneck\n","the transposed median shape of the dataset is  [ 20.50000067 401.02463054 401.02463054]\n","generating configuration for 3d_fullres\n","{0: {'batch_size': 2, 'num_pool_per_axis': [2, 6, 6], 'patch_size': array([ 16, 320, 320]), 'median_patient_size_in_voxels': array([ 21, 401, 401]), 'current_spacing': array([2.5       , 0.39648438, 0.39648438]), 'original_spacing': array([2.5       , 0.39648438, 0.39648438]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}}\n","transpose forward [0, 1, 2]\n","transpose backward [0, 1, 2]\n","Initializing to run preprocessing\n","npz folder: /content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_cropped_data/Task101_SCGM\n","output_folder: /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 450\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","2 2444\n","\n","1 606\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc07-mask-r2.npz\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","2 2734\n","1 410\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc08-mask-r3.npz\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: 2 3262\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc09-mask-r4.npz\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 464\n","2 2368\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc02-mask-r2.npz\n","1 476\n","2 2212\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc04-mask-r4.npz\n","1 540\n","1 396\n","2 2888\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc01-mask-r1.npz\n","2 2640\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc06-mask-r1.npz\n","1 474\n","2 2156\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc03-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: 1 498\n","2 2448\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc07-mask-r3.npz\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 482\n","2 2674\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc08-mask-r4.npz\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 552\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","2 2468\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc06-mask-r2.npz\n","1 482\n","2 2462\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc02-mask-r3.npz\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: \n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 472\n","2 2052\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc03-mask-r4.npz\n","1 526\n","2 2552\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc01-mask-r2.npz\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 422\n","2 2782\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc05-mask-r1.npz\n","1 342\n","2 2270\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc10-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 432\n","separate z, order in z is 0 order inplane is 1\n","2 3472\n","1 452\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc09-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","2 2378\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc07-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 500\n","1 574\n","2 2622\n","2 2406\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc02-mask-r4.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc06-mask-r3.npz\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 524\n","2 2746\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc01-mask-r3.npz\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 470\n","2 2350\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc04-mask-r1.npz\n","1 470\n","2 1962\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc10-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","1 450\n","2 2450\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc05-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","separate z, order in z is 0 order inplane is 1\n","1 462\n","separate z, order in z is 0 order inplane is 1\n","2 3178\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc09-mask-r2.npz\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 420\n","2 2952\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc08-mask-r1.npz\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 520\n","2 2450\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc06-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 482\n","1 460\n","2 2198\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc03-mask-r1.npz\n","2 2632\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc01-mask-r4.npz\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 506\n","2 2168\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc04-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 542\n","2 2066\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc10-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 488\n","2 2660\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc05-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 546\n","2 3248\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc09-mask-r3.npz\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 574\n","2 2448\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc08-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 372\n","2 2636\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc07-mask-r1.npz\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 498\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: 1 376\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","2 2008\n","2 2612\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc02-mask-r1.npz\n","1 554\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc03-mask-r2.npz\n","2 2246\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc04-mask-r3.npz\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 394\n","2 2064\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc10-mask-r4.npz\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 6, 126, 126)} \n","\n","1 416\n","2 2608\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site1-sc05-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","1 2004\n","separate z, order in z is 0 order inplane is 1\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc01-mask-r1.npz\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 1\n","1 2067\n","2 8880\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc07-mask-r2.npz\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 30, 401, 401)} \n","\n","1 3447\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc03-mask-r3.npz\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 36, 401, 401)} \n","\n","1 3807\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc02-mask-r2.npz\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after: 1 3747\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc09-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","1 3678\n","1 3372\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc08-mask-r3.npz\n","1 3420\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc06-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc04-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","1 2070\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc01-mask-r2.npz\n","1 2229\n","2 9486\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc07-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 30, 401, 401)} \n","\n","1 3045\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc03-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","1 2466\n","2 9543\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc01-mask-r3.npz\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","1 1926\n","2 9039\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc07-mask-r4.npz\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 36, 401, 401)} \n","\n","1 4263\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc02-mask-r3.npz\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 33, 401, 401)} \n","\n","1 2421\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc10-mask-r1.npz\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3543\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc08-mask-r4.npz\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3582\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 42, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 3\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc06-mask-r2.npz\n","1 3999\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc05-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 2031\n","separate z, order in z is 0 order inplane is 1\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc01-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","1 3474\n","separate z, order in z is 0 order inplane is 1\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc04-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 2886\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc08-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 33, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 36, 401, 401)} \n","\n","1 3063\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc10-mask-r2.npz\n","1 3420\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc02-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3387\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc09-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 4074\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc06-mask-r3.npz\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 36, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 42, 401, 401)} \n","\n","1 3726\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc02-mask-r1.npz\n","1 4452\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc05-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3471\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc04-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3285\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc08-mask-r2.npz\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 30, 401, 401)} \n","\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 33, 401, 401)} \n","\n","1 2748\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc03-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","1 3081\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc10-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3735\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc09-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 3477\n","1 892\n","2 6305\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc01-mask-r1.npz\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc06-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 42, 401, 401)} \n","\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","1 4887\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc05-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 30, 401, 401)} \n","\n","1 2826\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc03-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 33, 401, 401)} \n","\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 2769\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc10-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","1 3759\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc04-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","1 1428\n","2 9767\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc02-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 21, 401, 401)} \n","\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 1887\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc07-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","1 1493\n","2 8274\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc01-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 39, 401, 401)} \n","\n","1 3933\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc09-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 42, 401, 401)} \n","\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1773\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc03-mask-r3.npz\n","1 4116\n","separate z, order in z is 0 order inplane is 1\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site2-sc05-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","1 1739\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc02-mask-r3.npz\n","1 1220\n","2 7473\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc04-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","1 890\n","2 6199\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc06-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1409\n","2 8641\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc07-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 1727\n","2 8809\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc01-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1629\n","2 9328\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc08-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 1218\n","2 8866\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc03-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","separate z, order in z is 0 order inplane is 1\n","1 1315\n","2 9188\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc02-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 985\n","2 7913\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc05-mask-r1.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","1 1381\n","2 7578\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc06-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1394\n","2 8236\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc09-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 1934\n","2 9757\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc07-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 1180\n","2 7065\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc01-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","1 1241\n","2 8027\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc08-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","1 898\n","2 6750\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc04-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","separate z, order in z is 0 order inplane is 1\n","1 988\n","2 7490\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc03-mask-r1.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 1470\n","2 9926\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc05-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 969\n","2 7149\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc10-mask-r1.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","1 1738\n","2 8593\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc06-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 1369\n","2 8537\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc07-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","1 999\n","2 7755\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc02-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1134\n","2 6788\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc09-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","1 1280\n","2 7917\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc04-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1505\n","2 9303\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc03-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 1835\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc05-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1467\n","2 8804\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc10-mask-r2.npz\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 2004\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc01-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","1 1232\n","2 7648\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc06-mask-r4.npz\n","1 947\n","2 6618\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc08-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 1730\n","2 8702\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc09-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","1 1487\n","2 9962\n","separate z, order in z is 0 order inplane is 1\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc04-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2510\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc02-mask-r2.npz\n","1 2154\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc01-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","1 1329\n","2 9878\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc05-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1718\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc10-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1061\n","separate z, order in z is 0 order inplane is 3\n","2 7228\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc07-mask-r1.npz\n","1 1339\n","separate z, order in z is 0 order inplane is 3\n","2 8118\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc08-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1909\n","2 9785\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc09-mask-r3.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","1 2846\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc03-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2692\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc01-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","1 1184\n","2 8878\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site3-sc10-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 3050\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc02-mask-r3.npz\n","1 2232\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc04-mask-r4.npz\n","1 2534\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc06-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2894\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc07-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2696\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc08-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2474\n","2 9970\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc01-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 1998\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc03-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","1 2424\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc09-mask-r4.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2252\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc02-mask-r4.npz\n","1 2544\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc05-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2772\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc06-mask-r2.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 3112\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc07-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2038\n","2 9790\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc08-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2054\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc02-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2478\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc04-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","separate z, order in z is 0 order inplane is 1\n","1 2372\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc10-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2054\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc03-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2872\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc05-mask-r2.npz\n","1 3300\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc06-mask-r3.npz\n","1 2732\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc09-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","separate z, order in z is 0 order inplane is 3\n","1 2406\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc07-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2662\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc10-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2746\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc04-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2092\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc08-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2234\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc03-mask-r2.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2550\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc06-mask-r4.npz\n","1 2922\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc09-mask-r2.npz\n","1 3306\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc05-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2788\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc10-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 2420\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc08-mask-r2.npz\n","1 3100\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc04-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 24, 378, 378)} \n","\n","1 1960\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc10-mask-r4.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 2556\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc07-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 28, 404, 404)} \n","\n","1 3338\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc09-mask-r3.npz\n","1 2362\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_stage0/site4-sc05-mask-r4.npz\n","not using nonzero mask for normalization\n","Are we using the nonzero maks for normalizaion? OrderedDict([(0, False)])\n","the median shape of the dataset is  [ 10.25000033 401.02463054 401.02463054]\n","the max shape in the dataset is  [ 20.99999599 485.51724138 410.48275862]\n","the min shape in the dataset is  [  2.99999828 126.10837438 126.10837438]\n","we don't want feature maps smaller than  4  in the bottleneck\n","the transposed median shape of the dataset is  [ 10.25000033 401.02463054 401.02463054]\n","[{'batch_size': 16, 'num_pool_per_axis': [6, 6], 'patch_size': array([448, 448]), 'median_patient_size_in_voxels': array([ 10, 401, 401]), 'current_spacing': array([5.        , 0.39648438, 0.39648438]), 'original_spacing': array([5.        , 0.39648438, 0.39648438]), 'pool_op_kernel_sizes': [[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'do_dummy_2D_data_aug': False}]\n","Initializing to run preprocessing\n","npz folder: /content/drive/My Drive/Colab Notebooks/nnUNet_raw_data_base/nnUNet_cropped_data/Task101_SCGM\n","output_folder: /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000191, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n"," {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","\n","normalization...\n","normalization done\n"," {'spacing': array([4.99999714, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","normalization done\n","\n","normalization...\n","normalization done\n","1 225\n","2 1222\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc07-mask-r2.npz\n","normalization done\n","normalization done\n","1 237\n","2 1078\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc03-mask-r3.npz\n","1 205\n","2 1631\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc09-mask-r4.npz\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","1 198\n","2 1444\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc01-mask-r1.npz\n","\n","normalization...\n","1 303\n","1 238\n","normalization done\n","1 270\n","2 1320\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc06-mask-r1.npz\n","2 1367\n","2 1106\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc04-mask-r4.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc08-mask-r3.npz\n","1 232\n","2 1184\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc02-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 171\n","separate z, order in z is 0 order inplane is 3\n","2 1135\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc10-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 276\n","2 1234\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc06-mask-r2.npz\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 249\n","2 1224\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc07-mask-r3.npz\n"," {'spacing': array([4.99999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","\n","normalization...\n","normalization done\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999714, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization...\n","1 211\n","2 1391\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","1 236\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc05-mask-r1.npz\n","normalization done\n","normalization done\n","2 1026\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc03-mask-r4.npz\n","1 241\n","2 1337\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc08-mask-r4.npz\n","normalization...\n","normalization done\n","1 241\n","2 1231\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc02-mask-r3.npz\n","1 263\n","2 1276\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc01-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 235\n","2 981\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc10-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 287\n","2 1311\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc06-mask-r3.npz\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 226\n","2 1189\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc07-mask-r4.npz\n","\n","normalization...\n","normalization done\n","1 225\n","2 1225\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc05-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 235\n","2 1175\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc04-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 3\n","1 250\n","separate z, order in z is 0 order inplane is 1\n","2 1203\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc02-mask-r4.npz\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 262\n","2 1373\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc01-mask-r3.npz\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000191, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 216\n","2 1736\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc09-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 271\n","2 1033\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc10-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 260\n","2 1225\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc06-mask-r4.npz\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","normalization done\n","1 244\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: 2 1330\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc05-mask-r3.npz\n"," {'spacing': array([4.99999714, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 210\n","2 1476\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc08-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 253\n","2 1084\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc04-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00000095, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 241\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","2 1316\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc01-mask-r4.npz\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 230\n","2 1099\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc03-mask-r1.npz\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000191, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 231\n","2 1589\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc09-mask-r2.npz\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([4.99999905, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999905, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999905, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 197\n","1 186\n","2 1032\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc10-mask-r4.npz\n","2 1318\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc07-mask-r1.npz\n","before: {'spacing': array([4.99999952, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after: separate z, order in z is 0 order inplane is 3\n"," {'spacing': array([4.99999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([4.99999714, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999714, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999714, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 208\n","separate z, order in z is 0 order inplane is 1\n","2 1304\n","1 287\n","2 1224\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc08-mask-r2.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc05-mask-r4.npz\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 277\n","2 1123\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc04-mask-r3.npz\n","before: {'spacing': array([5.00000143, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 188\n","2 1306\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc02-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00000191, 0.5       , 0.5       ]), 'spacing_transposed': array([5.00000191, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([5.00000191, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","before: {'spacing': array([4.99999857, 0.5       , 0.5       ]), 'spacing_transposed': array([4.99999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 3, 100, 100)} \n","after:  {'spacing': array([4.99999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 3, 126, 126)} \n","\n","normalization...\n","normalization done\n","1 249\n","normalization done\n","2 1004\n","1 273\n","2 1624\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc09-mask-r3.npz\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site1-sc03-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 668\n","2 3334\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc01-mask-r1.npz\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000286, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","1 689\n","2 2960\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc07-mask-r2.npz\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([7.50000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 10, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1149\n","2 4499\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc03-mask-r3.npz\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([7.50000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 401, 401)} \n","\n","normalization...\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000238, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","1 1269\n","normalization done\n","2 5273\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc02-mask-r2.npz\n","normalization done\n","1 1124\n","2 5251\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc06-mask-r1.npz\n","1 1140\n","2 4871\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc04-mask-r4.npz\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","normalization done\n","normalization done\n","separate z, order in z is 0 order inplane is 3\n","1 1226\n","1 1249\n","2 4939\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc08-mask-r3.npz\n","2 5174\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc09-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 690\n","2 3395\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc01-mask-r2.npz\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000286, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 743\n","2 3162\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc07-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([7.50000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 10, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1015\n","2 4435\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc03-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 822\n","2 3181\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc01-mask-r3.npz\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000286, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 642\n","2 3013\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc07-mask-r4.npz\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([7.50000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1421\n","2 5412\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc02-mask-r3.npz\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after: before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n"," {'spacing': array([7.49999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 11, 401, 401)} \n","\n","normalization...\n","normalization...\n","normalization done\n","normalization done\n","1 807\n","2 4659\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc10-mask-r1.npz\n","1 1194\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 401, 401)} \n","\n","normalization...\n","2 4697\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc06-mask-r2.npz\n","normalization...\n","normalization done\n","normalization done\n","1 1333\n","1 1181\n","2 6463\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc05-mask-r1.npz\n","2 4905\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc08-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 677\n","2 3450\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc01-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000238, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","separate z, order in z is 0 order inplane is 3\n","\n","normalization...\n","normalization done\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","1 1158\n","2 4873\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc04-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 962\n","2 5300\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc08-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([7.50000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 401, 401)} \n","\n","normalization...\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([7.49999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 11, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","normalization done\n","1 1140\n","2 5394\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc02-mask-r4.npz\n","1 1021\n","2 3953\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc10-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","1 1358\n","2 5096\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc06-mask-r3.npz\n","normalization done\n","1 1129\n","2 5613\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc09-mask-r1.npz\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1484\n","2 6652\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc05-mask-r2.npz\n","before: {'spacing': array([7.50000143, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000143, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 12, 318, 318)} \n","after:  {'spacing': array([7.50000143, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","normalization done\n","1 1242\n","2 5724\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc02-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000238, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1157\n","2 4749\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc04-mask-r2.npz\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after: normalization done\n"," {'spacing': array([7.50000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 10, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","1 1095\n","2 4739\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc08-mask-r2.npz\n","normalization done\n","1 916\n","2 4726\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc03-mask-r1.npz\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([7.49999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 11, 401, 401)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","1 1027\n","2 4337\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc10-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.5, 0.5, 0.5]), 'spacing_transposed': array([7.5, 0.5, 0.5]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1159\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","2 4970\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc06-mask-r4.npz\n","normalization done\n","1 1245\n","2 5149\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc09-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","normalization done\n","1 1629\n","2 6609\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc05-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000095, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000095, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 10, 318, 318)} \n","after:  {'spacing': array([7.50000095, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 10, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","normalization done\n","1 942\n","2 4395\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc03-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999952, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999952, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 11, 318, 318)} \n","after:  {'spacing': array([7.49999952, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 11, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 923\n","before: {'spacing': array([7.50000238, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000238, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000238, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","2 4030\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc10-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 1253\n","2 4858\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc04-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.50000286, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000286, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 7, 318, 318)} \n","after:  {'spacing': array([7.50000286, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 7, 401, 401)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 629\n","2 3349\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc07-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([7.50000048, 0.5       , 0.5       ]), 'spacing_transposed': array([7.50000048, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 13, 318, 318)} \n","after:  {'spacing': array([7.50000048, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 13, 401, 401)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after: 1 1311\n"," {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","2 5369\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc09-mask-r3.npz\n","normalization done\n","1 892\n","2 6305\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc01-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([7.49999857, 0.5       , 0.5       ]), 'spacing_transposed': array([7.49999857, 0.5       , 0.5       ]), 'data.shape (data is transposed)': (1, 14, 318, 318)} \n","after:  {'spacing': array([7.49999857, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 401, 401)} \n","\n","normalization...\n","normalization done\n","1 1372\n","2 6522\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site2-sc05-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 1428\n","separate z, order in z is 0 order inplane is 1\n","2 9767\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc02-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","normalization...\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","normalization...\n","normalization done\n","normalization done\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","1 1773\n","1 890\n","normalization done\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc03-mask-r3.npz\n","2 6199\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc06-mask-r1.npz\n","1 1220\n","normalization done\n","2 7473\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc04-mask-r4.npz\n","1 1409\n","2 8641\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc07-mask-r2.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1629\n","2 9328\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc08-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1394\n","2 8236\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc09-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","normalization done\n","1 1493\n","2 8274\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc01-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","1 1739\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc02-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 1218\n","2 8866\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc03-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","normalization...\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","1 1381\n","normalization done\n","2 7578\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc06-mask-r2.npz\n","normalization done\n","1 1934\n","2 9757\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc07-mask-r3.npz\n","1 985\n","2 7913\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc05-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1241\n","2 8027\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc08-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","1 969\n","2 7149\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc10-mask-r1.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","normalization done\n","1 1727\n","2 8809\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc01-mask-r3.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","normalization...\n","normalization done\n","1 1315\n","2 9188\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc02-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 898\n","2 6750\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc04-mask-r1.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","normalization...\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1738\n","2 8593\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc06-mask-r3.npz\n","1 1369\n","2 8537\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc07-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","1 1470\n","2 9926\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc05-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1134\n","2 6788\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc09-mask-r1.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 1467\n","2 8804\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc10-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","normalization done\n","1 1180\n","separate z, order in z is 0 order inplane is 3\n","2 7065\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc01-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 3\n","1 988\n","2 7490\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc03-mask-r1.npz\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","normalization done\n","1 1280\n","2 7917\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc04-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 947\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 651)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 410)} \n","\n","normalization...\n","2 6618\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc08-mask-r1.npz\n","normalization done\n","separate z, order in z is 0 order inplane is 3\n","1 1232\n","2 7648\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc06-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","1 1835\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc05-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1730\n","separate z, order in z is 0 order inplane is 1\n","2 8702\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc09-mask-r2.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1718\n","2 10000\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc10-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 410)} \n","\n","normalization...\n","normalization done\n","1 999\n","2 7755\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc02-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 3\n","normalization done\n","1 1505\n","2 9303\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc03-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 769, 650)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 485, 410)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 3\n","1 1487\n","2 9962\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc04-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","1 1339\n","2 8118\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc08-mask-r2.npz\n","normalization done\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","1 1061\n","2 7228\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc07-mask-r1.npz\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","1 1002\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 770, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 486, 409)} \n","\n","2 5598\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc01-mask-r1.npz\n","normalization...\n","normalization done\n","1 1329\n","2 9878\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc05-mask-r4.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 649)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","normalization done\n","1 1909\n","2 9785\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc09-mask-r3.npz\n","before: {'spacing': array([2.5 , 0.25, 0.25]), 'spacing_transposed': array([2.5 , 0.25, 0.25]), 'data.shape (data is transposed)': (1, 20, 768, 648)} \n","after:  {'spacing': array([2.5       , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 20, 484, 409)} \n","\n","normalization...\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1255\n","2 5575\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc02-mask-r2.npz\n","1 1184\n","2 8878\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site3-sc10-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","1 1077\n","2 5167\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc01-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1423\n","separate z, order in z is 0 order inplane is 1\n","2 5332\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc03-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1116\n","2 6226\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc04-mask-r4.npz\n","1 1267\n","2 7534\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc06-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1525\n","1 1447\n","2 5409\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc02-mask-r3.npz\n","2 6155\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc07-mask-r2.npz\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","1 1348\n","2 5347\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc08-mask-r3.npz\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","1 1346\n","2 5151\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc01-mask-r3.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1212\n","2 5730\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc09-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","1 1019\n","2 4895\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc08-mask-r4.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1237\n","2 4985\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc01-mask-r4.npz\n","normalization done\n","1 999\n","2 5269\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc03-mask-r4.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1272\n","normalization done\n","2 6079\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc05-mask-r1.npz\n","1 1386\n","2 7191\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc06-mask-r2.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1126\n","normalization done\n","2 5060\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc02-mask-r4.npz\n","1 1556\n","2 6167\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc07-mask-r3.npz\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","1 1186\n","2 6424\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc10-mask-r1.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","1 1366\n","2 6324\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc09-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1027\n","2 6067\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc02-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1239\n","1 1331\n","2 6108\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc10-mask-r2.npz\n","2 6917\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc04-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","normalization done\n","1 1650\n","1 1436\n","2 5824\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc05-mask-r2.npz\n","2 6885\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc06-mask-r3.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","1 1027\n","2 5838\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc03-mask-r1.npz\n","normalization done\n","1 1203\n","2 5641\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc07-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","separate z, order in z is 0 order inplane is 1\n","normalization done\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","1 1461\n","normalization done\n","2 6332\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc09-mask-r2.npz\n","1 1394\n","2 6126\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc10-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","1 1373\n","2 6370\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc04-mask-r2.npz\n","normalization done\n","1 1046\n","2 5652\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc08-mask-r1.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","1 1275\n","2 6529\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc06-mask-r4.npz\n","normalization done\n","1 1653\n","2 5498\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc05-mask-r3.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1117\n","separate z, order in z is 0 order inplane is 3\n","2 5584\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc03-mask-r2.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 1\n","separate z, order in z is 0 order inplane is 3\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","separate z, order in z is 0 order inplane is 3\n","1 980\n","2 5473\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc10-mask-r4.npz\n","separate z, order in z is 0 order inplane is 3\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after: separate z, order in z is 0 order inplane is 1\n"," {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","1 1669\n","2 5966\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc09-mask-r3.npz\n","separate z, order in z is 0 order inplane is 1\n","before: {'spacing': array([5.00024986, 0.29296875, 0.29296875]), 'spacing_transposed': array([5.00024986, 0.29296875, 0.29296875]), 'data.shape (data is transposed)': (1, 12, 512, 512)} \n","after:  {'spacing': array([5.00024986, 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 12, 378, 378)} \n","\n","normalization...\n","normalization done\n","1 1210\n","2 5250\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc08-mask-r2.npz\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","normalization done\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","1 1550\n","2 6484\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc04-mask-r3.npz\n","normalization done\n","before: {'spacing': array([5.       , 0.2857143, 0.2857143]), 'spacing_transposed': array([5.       , 0.2857143, 0.2857143]), 'data.shape (data is transposed)': (1, 14, 560, 560)} \n","after:  {'spacing': array([5.        , 0.39648438, 0.39648438]), 'data.shape (data is resampled)': (1, 14, 404, 404)} \n","\n","normalization...\n","1 1278\n","2 6540\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc07-mask-r1.npz\n","normalization done\n","1 1181\n","2 5702\n","saving:  /content/drive/My Drive/Colab Notebooks/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1_2D_stage0/site4-sc05-mask-r4.npz\n"]}],"source":["# verify that the dataset will work & create plans for configuration 3d_fullres - this may take about 10 minutes\n","!nnUNetv2_plan_and_preprocess -d 501 -np 4 -c 3d_fullres --verify_dataset_integrity"]},{"cell_type":"markdown","metadata":{"id":"yW8MX96FAmjO"},"source":["## 7.6 Dataset Visualization\n","At this stage it is useful to examine the training and testing data.\n","\n","Here is an example for how to do this in python, however we would encourage you to visualize the data with MITK-Workbench, since it allows much more interaction with the data and therefore better understanding."]},{"cell_type":"markdown","metadata":{"id":"JAdAHMdMA4eR"},"source":["Train Data (with Labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":499},"executionInfo":{"elapsed":939,"status":"ok","timestamp":1643041791697,"user":{"displayName":"Carsten L","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"16100963491359375963"},"user_tz":-60},"id":"vAzrqTrkAooq","outputId":"652abcd1-82d2-4450-db09-5f1b67787f4f"},"outputs":[{"name":"stdout","output_type":"stream","text":["(100, 100, 3) (100, 100, 3)\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1440x576 with 6 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["#visualizing some of the training images and labels\n","# (re-run to see random pick-ups)\n","# only maximum of first 5 slices are plotted\n","train_img_name = os.listdir(train_image_dir)[np.random.randint(0,160)]\n","train_img = np.array(nib.load(os.path.join(train_image_dir,train_img_name)).dataobj)[:,:,:5]\n","train_label_name = train_img_name[:train_img_name.find('_0000.nii.gz')]+'.nii.gz'\n","train_label = np.array(nib.load(os.path.join(train_label_dir,train_label_name)).dataobj)[:,:,:5]\n","\n","print(train_img.shape,train_label.shape)\n","\n","max_rows = 2\n","max_cols = train_img.shape[2]\n","\n","fig, axes = plt.subplots(nrows=max_rows, ncols=max_cols, figsize=(20,8))\n","for idx in range(max_cols):\n","    axes[0, idx].axis(\"off\") \n","    axes[0, idx].set_title('Train Image'+str(idx+1))\n","    axes[0 ,idx].imshow(train_img[:,:,idx], cmap=\"gray\")\n","for idx in range(max_cols):    \n","    axes[1, idx].axis(\"off\")\n","    axes[1, idx].set_title('Train Label'+str(idx+1))\n","    axes[1, idx].imshow(train_label[:,:,idx])\n","\n","plt.subplots_adjust(wspace=.1, hspace=.1)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"4I5iZ03pAxtO"},"source":["Note: In the label image, yellow color represents white Matter and green-ish color represents grey matter."]},{"cell_type":"markdown","metadata":{"id":"Dup4exmGvPZW"},"source":["### 7.6.1 Sighting of Data and Lables with MITK-Workbench\n","At this stage it is also useful to analyze the imaging data with MITK-Workbench.\n","To do this, download the training data to your own local machine and visualize them via drag and drop into MITK-Workbench\n","\n","This is already explained in part 6.3.4"]},{"cell_type":"markdown","metadata":{"id":"IeeY1Yh_BV14"},"source":["## 7.7 Training nnU-Net\n","\n","nnU-Net stores a checkpoint every 50 epochs. If you need to continue a previous training, just add a -c to the training command.\n","\n","Generic Training Commands:\n","\n","```nnUNetv2_train Dataset_NAME_OR_ID CONFIGURATION FOLD -tr TRAINER_CLASS_NAME (additional options)```\n","\n","For 2D:  ```nnUNetv2_train DATASET_NAME_OR_ID 2d FOLD```\n","\n","For 3D Full resolution: ```nnUNetv2_train DATASET_NAME_OR_ID 3d_fullres FOLD```\n","\n","For Cascaded 3D:\n","\n","First Run lowres: ```nnUNetv2_train DATASET_NAME_OR_ID 3d_lowres FOLD```\n","\n","Then Run fullres: ```nnUNetv2_train DATASET_NAME_OR_ID 3d_cascade_fullres FOLD```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nNWpcAnVBDhX","outputId":"7a7d58ac-f727-4a4a-b29a-ddf7fa7964a8"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","###############################################\n","I am running the following nnUNet: 3d_fullres\n","My trainer class is:  <class 'nnunet.training.network_training.nnUNetTrainerV2.nnUNetTrainerV2'>\n","For that I will be using the following configuration:\n","num_classes:  2\n","modalities:  {0: 'MRI'}\n","use_mask_for_norm OrderedDict([(0, False)])\n","keep_only_largest_region None\n","min_region_size_per_class None\n","min_size_per_class None\n","normalization_schemes OrderedDict([(0, 'nonCT')])\n","stages...\n","\n","stage:  0\n","{'batch_size': 2, 'num_pool_per_axis': [2, 6, 6], 'patch_size': array([ 16, 320, 320]), 'median_patient_size_in_voxels': array([ 21, 401, 401]), 'current_spacing': array([2.5       , 0.39648438, 0.39648438]), 'original_spacing': array([2.5       , 0.39648438, 0.39648438]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}\n","\n","I am using stage 0 from these plans\n","I am using sample dice + CE loss\n","\n","I am using data from this folder:  /content/nnUNet_preprocessed/Task101_SCGM/nnUNetData_plans_v2.1\n","###############################################\n","2022-01-24 16:30:04.509631: Using dummy2d data augmentation\n","loading dataset\n","loading all case properties\n","2022-01-24 16:30:04.760235: Creating new 5-fold cross-validation split...\n","2022-01-24 16:30:04.765513: Desired fold for training: 0\n","2022-01-24 16:30:04.768678: This split has 128 training and 32 validation cases.\n","unpacking dataset\n","done\n","2022-01-24 16:31:18.840379: lr: 0.01\n","using pin_memory on device 0\n","using pin_memory on device 0\n","2022-01-24 16:31:58.841006: Unable to plot network architecture:\n","2022-01-24 16:31:58.847499: No module named 'hiddenlayer'\n","2022-01-24 16:31:58.854697: \n","printing the network instead:\n","\n","2022-01-24 16:31:58.908292: Generic_UNet(\n","  (conv_blocks_localization): ModuleList(\n","    (0): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (1): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (2): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (3): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (4): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","    (5): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n","            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n","            (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","  )\n","  (conv_blocks_context): ModuleList(\n","    (0): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n","          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n","          (instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (1): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(32, 64, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1))\n","          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n","          (instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (2): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (3): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (4): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (5): StackedConvLayers(\n","      (blocks): Sequential(\n","        (0): ConvDropoutNormNonlin(\n","          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","        (1): ConvDropoutNormNonlin(\n","          (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","          (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","        )\n","      )\n","    )\n","    (6): Sequential(\n","      (0): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","      (1): StackedConvLayers(\n","        (blocks): Sequential(\n","          (0): ConvDropoutNormNonlin(\n","            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n","            (instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n","            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n","          )\n","        )\n","      )\n","    )\n","  )\n","  (td): ModuleList()\n","  (tu): ModuleList(\n","    (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2), bias=False)\n","    (1): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2), bias=False)\n","    (2): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n","    (3): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n","    (4): ConvTranspose3d(128, 64, kernel_size=(1, 2, 2), stride=(1, 2, 2), bias=False)\n","    (5): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2), bias=False)\n","  )\n","  (seg_outputs): ModuleList(\n","    (0): Conv3d(320, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (1): Conv3d(320, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (2): Conv3d(256, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (3): Conv3d(128, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (4): Conv3d(64, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","    (5): Conv3d(32, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n","  )\n",")\n","2022-01-24 16:31:58.928155: \n","\n","2022-01-24 16:31:58.944025: \n","epoch:  0\n"]}],"source":["# train nnU-Net with 3d_fullres model on the SCGM Task with Fold 0\n","# Again Training may take a while therefore it is advised to interrupt the training after some time (e.g. after 1 epoch)\n","# 1 Epoch takes approximately: over 30 minutes on GoogleColab -- you need GoogleColab Pro to run this completely\n","!nnUNetv2_train 501 3d_fullres 0 -tr nnUNetTrainer_1epoch"]},{"cell_type":"markdown","metadata":{"id":"FPHW8VZ0C7lP"},"source":["## 7.8 Inference Code\n","\n","nnUNet_find_best_configuration will print inference commands you need to use. The easiest way to run inference is to simply use these commands.\n","\n","For each of the desired configurations, run:\n","\n","```nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_NAME_OR_ID -m CONFIGURATION --save_npz```\n","\n","Only specify ```--save_npz``` if you intend to use ensembling. ```--save_npz``` will make the command save the softmax probabilities alongside of the predicted segmentation masks requiring a lot of disk space.\n","\n","Note: Please select a separate OUTPUT_FOLDER for each configuration!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ordg3hM7DGx6"},"outputs":[],"source":["# Optional\n","!nnUNetv2_find_best_configuration -d 501"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u44bFWQoDQLV"},"outputs":[],"source":["result_dir = os.path.join(os.getenv(\"RESULTS_FOLDER\"),'nnUNet_Prediction_Results',dataset_name)\n","make_if_dont_exist(result_dir)\n","\n","team_name = 'awesome_nnU-Net_team' #make sure to change for your own team name"]},{"cell_type":"markdown","metadata":{"id":"OFhCbIP2DXnj"},"source":["**Note**: \n","\n","If you interrupted the training go the given fold inside of the RESULTS_FOLDER for the task and:\n","1. rename **model_best.model.pkl** to **model_final_checkpoint.model.pkl** \n","2. rename **model_best.model** to **model_final_checkpoint.model** "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XH86gjTJDRks"},"outputs":[],"source":["#location where you want save your results, will be created if dont exist\n","!nnUNetv2_predict -i '${nnUNet_raw}/Dataset501_SCGM/imagesTs' -o '${RESULTS_FOLDER}/Dataset501/predTs' -d 501 -tr nnUNetTrainer_1epoch -c 3d_fullres "]},{"cell_type":"markdown","metadata":{"id":"LrybnwGkDzeA"},"source":["If you wish to run ensembling, you can ensemble the predictions from several configurations with the following command:\n","\n","```nnUNet_ensemble -f FOLDER1 FOLDER2 ... -o OUTPUT_FOLDER -pp POSTPROCESSING_FILE```"]},{"cell_type":"markdown","metadata":{"id":"Xkk_cgwpD4Tg"},"source":["## 7.9 Visualization of Predictions\n","Similar to the dataset visualization we would encourage you to do this with MITIK-Workbench.\n","\n","Here is a quick visualization with python."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5qBVpSIPED7b"},"outputs":[],"source":["#visualizing the predicted results\n","# (re-run to see random pick-ups)\n","# only maximum of first 5 slices are plotted\n","\n","test_img_name = os.listdir(test_dir)[np.random.randint(0,40)]\n","test_img = np.array(nib.load(os.path.join(test_dir,test_img_name)).dataobj)[:,:,:5]\n","predicted_img_name = test_img_name[:test_img_name.find('_0000.nii.gz')]+'.nii.gz'\n","predicted_label = np.array(nib.load(os.path.join(result_dir,predicted_img_name)).dataobj)[:,:,:5]\n","print('Test Image Shape: ',test_img.shape)\n","print(\"Predicted Image Shape:\",predicted_label.shape)\n","\n","max_rows = 2\n","max_cols = test_img.shape[2]\n","\n","fig, axes = plt.subplots(nrows=max_rows, ncols=max_cols, figsize=(20,8))\n","for idx in range(max_cols):\n","    axes[0, idx].axis(\"off\") \n","    axes[0, idx].set_title('Test Image'+str(idx+1))\n","    axes[0 ,idx].imshow(test_img[:,:,idx], cmap=\"gray\")\n","for idx in range(max_cols):    \n","    axes[1, idx].axis(\"off\")\n","    axes[1, idx].set_title('Predicted Label'+str(idx+1))\n","    axes[1, idx].imshow(predicted_label[:,:,idx])\n","    \n","plt.subplots_adjust(wspace=.1, hspace=.1)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"tnX5wrhC1Dgk"},"source":["# 8. Final Words\n","The Workshop and this tutorial is now finished.\n","\n","If you made it this far, we hope you enjoyed the ride.\n","\n","To further expand your knowledge about nnU-Net you may now want to tackle one of the following tasks:\n","- Use Pretrained nnU-Net models for other Decathlon Datasets.\n","- Look up a Dataset of your own and implement a new Dataset for nnU-Net.\n","- Download the nnU-Net repository to your local machine and familiarize yourself with the codebase.\n","\n","Please feel free to share it with people who you believe might benefit from it."]},{"cell_type":"markdown","metadata":{},"source":["# Acknowledgements\n","Many thanks to the people who tested this tutorial and gave feedback:\n","- Stephen Schaumann\n","- Robin Peretzke\n","- Santhosh P.\n","- Jonas Bohn\n","- Sebastian Ziegler\n","- Yannick Kirchhoff\n","- Tobias Norajitra\n","- Lars Krämer\n","- Peter Full\n","- Sebastian Zieger\n","- Lukas Klein\n","- Tassilo Wald\n","- Stefan Dinkelacker\n","- Fabian Isensee\n","- Paul Jäger"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"name":"nnU-Net_Workshop-IML.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}