Open AccessEPySeg: a coding-free solution for automated segmentation of epithelia using deep learning
Author(s) -
Benoît Aigouy,
Claudio Cortes,
Shanda Liu,
Benjamin Prud’homme
Publication year - 2020
Publication title -
development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.754
H-Index - 325
eISSN - 1477-9129
pISSN - 0950-1991
DOI - 10.1242/dev.194589
Subject(s) - biology , segmentation , python (programming language) , deep learning , morphogenesis , artificial intelligence , open source , graphical user interface , software , coding (social sciences) , computer science , computational biology , gene , operating system , biochemistry , statistics , mathematics
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 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.