Abstract
ABSTRACTEpithelia 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.
Highlights
Epithelia are dynamic tissues undergoing dramatic shape changes throughout their development
EPySeg comes with a complete and straightforward graphical user interface (GUI), allowing users that are curious about deep learning, as well as more advanced users, to build and train custom networks to achieve any segmentation paradigm of interest
Our network was trained on a large number of images of very divergent fly epithelia acquired using several microscopy setups to allow our segmentation paradigm to be robust and able to segment a broad range of epithelial tissues
Summary
Epithelia are dynamic tissues undergoing dramatic shape changes throughout their development. To address all these limitations, we present EPySeg, a coding-free solution to efficiently segment raw images of epithelial tissues, using a pre-trained neural network.
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