cubic 0.7.0a13

CUDA-accelerated 3D BioImage Computing

cubic

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, image-quality metrics (PSNR, SSIM, MicroSSIM, MS-SSIM), and other features.

Getting started

Dependencies

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

Installation

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]'
# plotting helpers (matplotlib)
pip install '.[plot]'
# run the example notebooks (jupyter, pooch)
pip install '.[examples]'
# developer tools (pre-commit, pytest)
pip install -e '.[dev]'
# install everything
pip install -e '.[all]'

Testing

Run style checks and tests using pre-commit and pytest:

pre-commit run --all-files
pytest

Contributing

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.

Usage

Example Notebooks

Notebook	Description
Resolution Estimation (2D)	FRC and DCR on STED microscopy data
Resolution Estimation (3D)	FSC and DCR on 3D confocal pollen data
Split Comparison (FRC/FSC)	Checkerboard vs binomial splitting for single-image FRC/FSC
Deconvolution Iterations (3D)	RL deconvolution stopping criteria via PSNR, SSIM, FSC, DCR
3D Monolayer Segmentation	3D nuclei and cell segmentation of hiPSC monolayer
3D Feature Extraction	GPU-accelerated regionprops on 3D fluorescence data

Citation

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}
}