Transforming BioImage Analysis: the AI4Life approach Talk for Cell Physics 2025

Description: Slides for the talk “Transforming BioImage Analysis: The AI4Life Approach,” presented on 09 October 2025 at Cell Physics. The presentation overviews AI4Life’s user-centred workflows and zero-code tools for bioimage analysis, FAIR model sharing via the BioImage Model Zoo, and practical pipelines for denoising, segmentation (including Cellpose retraining/export), and tracking with TrackMate. Resources and information of the presented use case: GitHub Repository of the use case: oc-1-project-11. Dataset in the BioImage Archive can be found under the S-BIAD1377 id. Cellpose model uploaded to the bioimage.io under the happy-elephant id. Additional information about this use case and other use cases similar to this one, can be found on the AI4Life website. Tools presented in this talk and its documentation: BioImageArchive - Website and a very useful course. N2V (deprecated) - GitHub & References. CAREamics - official docs (PyTorch denoising suite). CellPose - user & API documentation. Trackmate - plugin docs; TrackMate-Cellpose integration guide. BioImage.io - BioImage Model Zoo docs. Acknowledgements: The data was provided by Madeline Lovejoy and Rafael Garcia-Mata (PhD) at Garcia-Mata Lab, University of Toledo (USA). Special thanks to the Human Technopole team, specially to Vera Galinova and Mehdi Seifi for his assistance and the development of the use case. AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.