Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations

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Item Type:	Article
Title:	Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
Creators Name:	Malin-Mayor, C., Hirsch, P., Guignard, L., McDole, K., Wan, Y., Lemon, W.C., Kainmueller, D., Keller, P.J., Preibisch, S. and Funke, J.
Abstract:	We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.
Keywords:	Cell Lineage, Image Processing, Machine Learning, Animals, Mice
Source:	Nature Biotechnology
ISSN:	1087-0156
Publisher:	Nature Publishing Group
Volume:	44
Number:	1
Page Range:	44-49
Date:	January 2023
Official Publication:	https://doi.org/10.1038/s41587-022-01427-7
PubMed:	View item in PubMed

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