EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning | Development | The Company of Biologists

Benoit Aigouy, Claudio Cortes, Shanda Liu, Benjamin Prud'Homme; EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. Development 15 December 2020; 147 (24): dev194589. doi: https://doi.org/10.1242/dev.194589 Download citation file: Epithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually involves extensive manual correction, even with semi-automated tools. Here, we present EPySeg, an open-source, coding-free software that uses deep learning to segment membrane-stained epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a Python package on a local compu

Benoit Aigouy, Claudio Cortes, Shanda Liu, Benjamin Prud'Homme; EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. Development 15 December 2020; 147 (24): dev194589. doi: https://doi.org/10.1242/dev.194589 Download citation file: Epithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its con