Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 36065022.
- Also identified by DOI 10.1038/s41587-022-01427-7 and PMC identifier 7614077.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Embryo, Mammalian
- Blastocyst