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Journal Articles Development (Cambridge, England) Year : 2020

EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning

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Abstract

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 implies 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 computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By substantially reducing human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

Dates and versions

hal-03277942 , version 1 (05-07-2021)

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Benoit Aigouy, Claudio Cortes, Shanda Liu, Benjamin Prud'Homme. EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. Development (Cambridge, England), 2020, ⟨10.1242/dev.194589⟩. ⟨hal-03277942⟩
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