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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. and Hirsch, P. and Guignard, L. and McDole, K. and Wan, Y. and Lemon, W.C. and Kainmueller, D. and Keller, P.J. and 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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