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 users to add GPU acceleration to existing codebases by simply replacing import statements and transferring input arrays to the target device. It also provides custom GPU-accelerated implementations of additional features, including Fourier Ring and Shell Correlation for image resolution, faster Richardson-Lucy deconvolution, average precision (AP) for segmentation quality assessment, and other features.

• Python >=3.10
• numpy/scipy/scikit-image
• [optional] CUDA>=11.x, CuPy, cuCIM
• [optional] Cellpose for segmentation

Install optional CUDA dependencies if GPU support is needed.

Install from PyPI:

pip install cubic

Or install from source:

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
pytest

Contributions and bug reports are welcome. Install development dependencies and set up pre-commit hooks:

pip install -e '.[dev]'
pre-commit install

Pre-commit will then run style checks automatically. Please open an issue or pull request on GitHub.

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}
}