cubic is a Python library that accelerates processing and analysis of
multidimensional (2D/3D+) bioimages using CUDA.
By leveraging GPU-enabled operations where possible, it offers substantial
speed ups over purely CPU-based approaches.
cubic's device-agnostic API wraps scipy/scikit-image and cupy/cuCIM,
allowing to add GPU acceleration to existing codebases by simply replacing import
statement and and transferring input arrays to the target device.
It also provides custom GPU-accelerated implementations of additional
features, including Forier Ring and Shell Correlation for image resolution,
faster Richardson-Lucy deconvolution, average precision (AP) for segmentation
quality assesement, and other.
- Python >=3.10
- numpy/scipy/scikit-image
- [optional] CUDA 11.x-12.x, cupy, cuCIM
- [optional] Cellpose for segmentation
Clone the repository and install the base library:
git clone https://github.com/alxndrkalinin/cubic.git
cd cubic
pip install .Optional extras from pyproject.toml enable additional functionality:
# mesh feature extraction
pip install '.[mesh]'
# segmentation via Cellpose
pip install '.[cellpose]'
# developer tools (pre-commit, pytest)
pip install -e '.[dev]'
# install everything
pip install -e '.[all]'Run style checks and tests using pre-commit and pytest:
pre-commit run --all-files
pytestContributions and bug reports are welcome. Install development dependencies and set up pre-commit hooks:
pip install -e '.[dev]'
pre-commit installPre-commit will then run style checks automatically. Please open an issue or pull request on GitHub.
See the example notebooks in examples/notebooks/.
If you use cubic in your research, please cite it:
@inproceedings{kalinin2025cubic,
title={cubic: CUDA-accelerated 3D BioImage Computing},
author={Kalinin, Alexandr A and Carpenter, Anne E and Singh, Shantanu and O’Meara, Matthew J},
booktitle={International Conference on Computer Vision Workshops (ICCVW)},
year={2025},
organization={IEEE}
}