Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
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Name of publisher, distributor, etc.
Nature Publishing Group US,
Date of publication, distribution, etc.
2022-09-05.
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General note
/pmc/articles/PMC7614077/
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General note
/pubmed/36065022
520 ## - SUMMARY, ETC.
Summary, etc.
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.
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction
https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
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Language note
en
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