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Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
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...
| Autores principales: | , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group US
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614077/ https://www.ncbi.nlm.nih.gov/pubmed/36065022 http://dx.doi.org/10.1038/s41587-022-01427-7 |