Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
Caroline Malin-Mayor1, Peter Hirsch2,3, Leo Guignard1,4
1HHMI Janelia, Ashburn, VA, USA.
Nature Biotechnology
|September 6, 2022
Abstract
None:
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.
