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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. and Hirsch, P. and Guignard, L. and McDole, K. and Wan, Y. and Lemon, W.C. and Kainmueller, D. and Keller, P.J. and 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
Date:5 September 2022
Official Publication:https://doi.org/10.1038/s41587-022-01427-7
PubMed:View item in PubMed
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http://edoc.mdc-berlin.de/20794/Preprint version

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