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Nature Biotechnology. 2022 Sep 05;. doi: 10.1038/s41587-022-01427-7
Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations. Keller LabScientific Computing
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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.
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Janelia Authors
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