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

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.

SUBMITTER: Malin-Mayor C 

PROVIDER: S-EPMC7614077 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature