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bioRxiv · 10.1101/2020.06.30.179507

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

Aigouy, B.·Prud'homme, B.
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Abstract

Epithelia are dynamic tissues that self-remodel during their development. At 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 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 alleviating human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

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Aigouy, B., Prud'homme, B.. 2020-07-02. EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. https://doi.org/10.1101/2020.06.30.179507

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