Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Correspondence
  • Published:

BiaPy: accessible deep learning on bioimages

Nature Methods volume 22, pages 1124–1126 (2025)Cite this article

Subjects

Bioimage analysis is a cornerstone of modern life sciences, powering discoveries and insights derived from biological image data. Deep learning has become an invaluable tool for analyzing microscopy datasets, and its application is increasingly widespread in biomedical research1. However, its prerequisite for high-level programming skills has often acted as a barrier, limiting accessibility for researchers without a specific computational background2.

The rapid evolution of deep learning methods, along with the diverse range of bioimage analysis applications, has created a dynamic landscape that requires researchers to adapt continuously. These applications often involve navigating multiple tools and integrating workflows that handle tasks such as image segmentation, object detection, tracking, image classification and reconstruction. To support this diversity, many solutions now either build on or integrate deep learning approaches in some form (Supplementary Table 1). For example, deepImageJ3 allows users to apply (but not retrain) pretrained deep learning models within Fiji4, leveraging resources like the BioImage Model Zoo5 for model sharing. Web-based platforms such as ZeroCostDL4Mic6 and ImJoy7 provide easy access to deep learning workflows, though their reliance on cloud-based services can limit flexibility and reproducibility. Alternatively, tools like Ilastik8 offer intuitive graphical interfaces that make deep learning-based image analysis more accessible to non-expert users.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: BiaPy environment and scope.

Code availability

The complete source code for the BiaPy platform, encompassing the library’s main code, GUI and associated documentation, is accessible at https://github.com/BiaPyX. For comprehensive documentation, video tutorials and use-case examples, please refer to BiaPy’s documentation website (https://biapy.readthedocs.io/en/latest/).

References

  1. Esteva, A. et al. npj Digit. Med. 4, 5 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Nogare, D. D., Hartley, M., Deschamps, J., Ellenberg, J. & Jug, F. Nat. Methods 20, 973–975 (2023).

    Article  CAS  PubMed  Google Scholar 

  3. Gómez-de-Mariscal, E. et al. Nat. Methods 18, 1192–1195 (2021).

    Article  PubMed  Google Scholar 

  4. Schindelin, J. et al. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Ouyang, W. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.06.07.495102 (2022).

  6. von Chamier, L. et al. Nat. Commun. 12, 2276 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. & Zimmer, C. Nat. Methods 16, 1199–1200 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Berg, S. et al. Nat. Methods 16, 1226–1232 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Hidalgo-Cenalmor, I. et al. Nat. Methods 21, 925–927 (2024).

    Article  CAS  PubMed  Google Scholar 

  10. Cimini, B. A. & Eliceiri, K. W. Nat. Methods 20, 976–978 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work is partially supported by grant GIU23/022 (to I.A.-C.) funded by the University of the Basque Country (UPV/EHU), grants PID2021-126701OB-I00 (to I.A-C.) and PID2023152631OB-I00 (to A.M.-B.), funded by the Ministerio de Ciencia, Innovación y Universidades, AEI, MCIN/AEI/10.13039/501100011033, and by “ERDF A way of making Europe”. P.G.G. has been funded by Margarita Salas Fellowship – NextGenerationEU. J.A.A.-S.R.’s work has been funded by the Junta de Andalucía (Consejería de economía, conocimiento, empresas y Universidad) grant PY18-631 co-funded by FEDER funds. L.M.E. thanks the PIE-202120E047-Conexiones-Life network for its networking and input. I.H-C. and A.M-B. received funding from the European Union through the Horizon Europe program (AI4LIFE project with grant agreement 101057970-AI4LIFE). Funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. I.H-C. acknowledges the support of the Gulbenkian Foundation (Fundação Calouste Gulbenkian).

Author information

Authors and Affiliations

  1. MRC Laboratory of Molecular Biology, University of Cambridge, Cambridge, UK

    Daniel Franco-Barranco & Pedro Gómez-Gálvez

  2. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Daniel Franco-Barranco & Pedro Gómez-Gálvez

  3. Donostia International Physics Center (DIPC), San Sebastian, Spain

    Daniel Franco-Barranco, Aitor González-Marfil & Ignacio Arganda-Carreras

  4. Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Daniel Franco-Barranco, Ivan Hidalgo-Cenalmor, Lenka Backová, Aitor González-Marfil & Ignacio Arganda-Carreras

  5. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Facultad de Biología, Universidad de Sevilla, Seville, Spain

    Jesús A. Andrés-San Román, Pedro Gómez-Gálvez & Luis M. Escudero

  6. Biomedical Network Research Centre on Neurodegenerative Diseases (CIBERNED), Madrid, Spain

    Jesús A. Andrés-San Román, Pedro Gómez-Gálvez & Luis M. Escudero

  7. Instituto Gulbenkian de Ciência, Oeiras, Portugal

    Ivan Hidalgo-Cenalmor

  8. Biofisika Institute (CSIC-UPV/EHU), Leioa, Spain

    Lenka Backová & Ignacio Arganda-Carreras

  9. Laboratoire d’Optique et Biosciences, CNRS, Inserm, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France

    Clément Caporal & Anatole Chessel

  10. Department of Computer Science, Boston College, Boston, MA, USA

    Donglai Wei

  11. Departamento de Bioingenieria, Universidad Carlos III de Madrid, Madrid, Spain

    Arrate Muñoz-Barrutia

  12. Area de Bioingenieria, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain

    Arrate Muñoz-Barrutia

  13. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Ignacio Arganda-Carreras

Authors
  1. Daniel Franco-Barranco
    View author publications

    Search author on:PubMed Google Scholar

  2. Jesús A. Andrés-San Román
    View author publications

    Search author on:PubMed Google Scholar

  3. Ivan Hidalgo-Cenalmor
    View author publications

    Search author on:PubMed Google Scholar

  4. Lenka Backová
    View author publications

    Search author on:PubMed Google Scholar

  5. Aitor González-Marfil
    View author publications

    Search author on:PubMed Google Scholar

  6. Clément Caporal
    View author publications

    Search author on:PubMed Google Scholar

  7. Anatole Chessel
    View author publications

    Search author on:PubMed Google Scholar

  8. Pedro Gómez-Gálvez
    View author publications

    Search author on:PubMed Google Scholar

  9. Luis M. Escudero
    View author publications

    Search author on:PubMed Google Scholar

  10. Donglai Wei
    View author publications

    Search author on:PubMed Google Scholar

  11. Arrate Muñoz-Barrutia
    View author publications

    Search author on:PubMed Google Scholar

  12. Ignacio Arganda-Carreras
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization: D.F.-B., A.C., L.M.E, D.W., A.M.-B. and I.A.-C. Software: D.F.-B., J.A.A.-S.R., I.H.-C., L.B., A.G.-M., P.G.-G., C.C. and I.A.-C. Validation: D.F.-B., I.H.-C., L.B., A.G.-M., P.G.-G., C.C. and I.A.-C. Writing — initial outline: D.F.-B., A.M.-B. and I.A.-C. Writing — original draft: D.F.-B., A.M.-B. and I.A.-C. Writing — review and editing: all authors. Visualization: D.F.-B. and I.A.-C. Supervision: L.M.E, D.W., A.M.-B. and I.A.-C. Funding acquisition: A.C., L.M.E., D.W., A.M.-B. and I.A.-C.

Corresponding authors

Correspondence to Arrate Muñoz-Barrutia or Ignacio Arganda-Carreras.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Perrine Paul-Gilloteaux, Curtis Rueden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

Supplementary Information (download PDF )

Supplementary Fig. 1, Table 1 and Technical Description

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Franco-Barranco, D., Andrés-San Román, J.A., Hidalgo-Cenalmor, I. et al. BiaPy: accessible deep learning on bioimages. Nat Methods 22, 1124–1126 (2025). https://doi.org/10.1038/s41592-025-02699-y

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41592-025-02699-y

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics