Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
Autor/innen
- C. Malin-Mayor
- P. Hirsch
- L. Guignard
- K. McDole
- Y. Wan
- W.C. Lemon
- D. Kainmueller
- P.J. Keller
- S. Preibisch
- J. Funke
Journal
- Nature Biotechnology
Quellenangabe
- Nat Biotechnol 44 (1): 44-49
Zusammenfassung
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.