In:
Nature Biotechnology, Springer Science and Business Media LLC, Vol. 41, No. 1 ( 2023-01), p. 44-49
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
1087-0156
,
1546-1696
DOI:
10.1038/s41587-022-01427-7
Sprache:
Englisch
Verlag:
Springer Science and Business Media LLC
Publikationsdatum:
2023
ZDB Id:
1494943-X
ZDB Id:
1311932-1
SSG:
12