CellSeg3D: self-supervised 3D cell segmentation for fluorescence microscopy

• DOI: 10.7554/elife.99848
• Publication Date: 2024-09-06T15:27:03Z
• Authors: Cyril Achard; Timokleia Kousi; Markus Frey; Maxime Vidal; Yves Paychère; Colin Hofmann; Asim Iqbal; Sebastien B Hausmann; Stéphane Pagès; Mackenzie Weygand...

Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially neuroscience. Here, we introduce a novel 3D self-supervised learning method designed to address inherent complexity quantifying volumes, often cleared neural tissue. We offer new mesoSPIM dataset show that CellSeg3D can match state-of-the-art supervised methods. Our contributions are made accessible through Python package with full GUI integration napari.