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We demonstrate that our self-supervised model, WNet3D \u2013 trained without any ground truth labels \u2013 achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts.<\/jats:p>","DOI":"10.7554\/elife.99848","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T11:27:03Z","timestamp":1725622023000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.99848","source":"Crossref","is-referenced-by-count":13,"title":["CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy"],"prefix":"10.7554","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6992-6928","authenticated-orcid":true,"given":"Cyril","family":"Achard","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02s376052","id-type":"ROR","asserted-by":"publisher"}],"name":"Brain Mind Institute and Neuro X, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne 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