DiffAM: Differentiable Appearance Modeling

[Preprint] [Project Page] [SPIE MI 2024 Homepage] [DiffSD Technical Report (Korean)]

Official source code for SPIE Medical Image 2024 paper "Vertebral segmentation without training using differentiable appearance modeling of a deformable spine template."

How to run

### Clone the repository
$ mkdir diff-am
$ git clone https://github.com/SSTDV-Project/DiffAM.git .
$ cd diff-am

### Install & activate environment
$ conda env create -f environment.yml
$ conda activate diff-am

### Run examples
$ cd examples
$ python test-sphere.py

Tested environment

• OS: Ubuntu 20.04
• GPU: NVIDIA GeForce RTX 3090
• GPU Driver: 535.129.03
• Host CUDA version: 12.2

Citation

@inproceedings{10.1117/12.3006602,
  author = {Hyunsoo Kim and Jinah Park},
  title = {{Vertebral segmentation without training using differentiable appearance modeling of a deformable spine template}},
  volume = {12926},
  booktitle = {Medical Imaging 2024: Image Processing},
  editor = {Olivier Colliot and Jhimli Mitra},
  organization = {International Society for Optics and Photonics},
  publisher = {SPIE},
  pages = {129262N},
  keywords = {Deformable model, Differentiable appeance modeling, Differentiable signed distance operator, Spectral mesh optimization, Vertebral segmentation},
  year = {2024},
  doi = {10.1117/12.3006602},
  URL = {https://doi.org/10.1117/12.3006602}
}

Acknowledgement

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.00223446, Development of object-oriented synthetic data generation and evaluation methods)