@article {68313,
	title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.},
	journal = {Nature Biotechnology},
	year = {2022},
	month = {2022 Sep 05},
	abstract = {<p>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.</p>
},
	issn = {1546-1696},
	doi = {10.1038/s41587-022-01427-7},
	author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan}
}
