RT Journal Article
SR Electronic
T1 EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.30.179507
DO 10.1101/2020.06.30.179507
A1 Benoit Aigouy
A1 Benjamin Prud’Homme
YR 2020
UL http://biorxiv.org/content/early/2020/07/02/2020.06.30.179507.abstract
AB 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.