BiaPy: Bioimage analysis pipelines in Python

BiaPy is an open source Python library for building bioimage analysis pipelines, also called workflows. This repository is actively under development by the Biomedical Computer Vision group at the University of the Basque Country and the Donostia International Physics Center.

The library provides an easy way to create image processing pipelines that are commonly used in the analysis of biology microscopy images in 2D and 3D. Specifically, BiaPy contains ready-to-use solutions for tasks such as semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification. The source code is based on Pytorch as the backend. As BiaPy’s core is based on deep learning, it is recommended to use a machine with a graphics processing unit (GPU) for faster training and execution.

_images/BiaPy-workflow-examples.svg

Get started

Workflows

Tutorials

How to contribute

API

Citation

This repository is the base of the following works:

 @inproceedings{franco2023biapy,
    title={BiaPy: a ready-to-use library for Bioimage Analysis Pipelines},
    author={Franco-Barranco, Daniel and Andr{\'e}s-San Rom{\'a}n, Jes{\'u}s A and G{\'o}mez-G{\'a}lvez, Pedro and Escudero, Luis M and Mu{\~n}oz-Barrutia, Arrate and Arganda-Carreras, Ignacio},
    booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
    pages={1--5},
    year={2023},
    organization={IEEE}
 }

 @article {Andr{\'e}s-San Rom{\'a}n2023.01.05.522724,
    author = {Jes{\'u}s A. Andr{\'e}s-San Rom{\'a}n and Carmen Gordillo-V{\'a}zquez and Daniel Franco-Barranco and Laura Morato and Cecilia H. Fern{\'a}ndez-Espartero and Gabriel Baonza and Antonio Tagua and Pablo Vicente-Munuera and Ana M. Palacios and Mar{\'\i}a P. Gavil{\'a}n and Fernando Mart{\'\i}n-Belmonte and Valentina Annese and Pedro G{\'o}mez-G{\'a}lvez and Ignacio Arganda-Carreras and Luis M. Escudero},
    title = {CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia},
    elocation-id = {2023.01.05.522724},
    year = {2023},
    doi = {10.1101/2023.01.05.522724},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2023/08/31/2023.01.05.522724},
    eprint = {https://www.biorxiv.org/content/early/2023/08/31/2023.01.05.522724.full.pdf},
    journal = {bioRxiv}
 }

 @article{franco2022domain,
    title = {Deep learning based domain adaptation for mitochondria segmentation on EM volumes},
    journal = {Computer Methods and Programs in Biomedicine},
    volume = {222},
    pages = {106949},
    year = {2022},
    publisher={Elsevier}
    issn = {0169-2607},
    doi = {https://doi.org/10.1016/j.cmpb.2022.106949},
    url = {https://www.sciencedirect.com/science/article/pii/S0169260722003315},
    author={Franco-Barranco, Daniel and Pastor-Tronch, Julio and Gonz{\'a}lez-Marfil, Aitor and Mu{\~n}oz-Barrutia, Arrate and Arganda-Carreras, Ignacio},
 }

 @Article{Franco-Barranco2021,
    author={Franco-Barranco, Daniel and Muñoz-Barrutia, Arrate and Arganda-Carreras, Ignacio},
    title={Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes},
    journal={Neuroinformatics},
    year={2021},
    month={Dec},
    day={02},
    issn={1559-0089},
    doi={10.1007/s12021-021-09556-1},
    url={https://doi.org/10.1007/s12021-021-09556-1}
 }

@inproceedings{wei2020mitoem,
    title={MitoEM dataset: large-scale 3D mitochondria instance segmentation from EM images},
    author={Wei, Donglai and Lin, Zudi and Franco-Barranco, Daniel and Wendt, Nils and Liu, Xingyu and Yin, Wenjie and Huang, Xin and Gupta, Aarush and Jang, Won-Dong and Wang, Xueying and others},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={66--76},
    year={2020},
    organization={Springer}
}