期刊
DEVELOPMENT
卷 147, 期 24, 页码 -出版社
COMPANY BIOLOGISTS LTD
DOI: 10.1242/dev.194589
关键词
Computer vision; Deep learning; Epithelia; Quantitative biology; Segmentation; Software
资金
- Max Planck Core grant
- Fondation Leducq [15CVD01]
- Centre National de la Recherche Scientifique
- France-BioImaging/PICsL infrastructure [ANR-10-INSB-04-01]
- European Research Council under the European Union's Seventh Framework Programme [(FP/2007-2013)/ERC Grant] [615789]
- European Research Council (ERC) [615789] Funding Source: European Research Council (ERC)
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据