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| Item Type: | Article |
|---|---|
| Title: | Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations |
| Creators Name: | Malin-Mayor, C., Hirsch, P., Guignard, L., McDole, K., Wan, Y., Lemon, W.C., Kainmueller, D., Keller, P.J., Preibisch, S. and Funke, J. |
| 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. |
| Keywords: | Cell Lineage, Image Processing, Machine Learning, Animals, Mice |
| Source: | Nature Biotechnology |
| ISSN: | 1087-0156 |
| Publisher: | Nature Publishing Group |
| Volume: | 44 |
| Number: | 1 |
| Page Range: | 44-49 |
| Date: | January 2023 |
| Official Publication: | https://doi.org/10.1038/s41587-022-01427-7 |
| PubMed: | View item in PubMed |
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