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...

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Detalles Bibliográficos
Autores principales: Malin-Mayor, Caroline, Hirsch, Peter, Guignard, Leo, McDole, Katie, Wan, Yinan, Lemon, William C., Kainmueller, Dagmar, Keller, Philipp J., Preibisch, Stephan, Funke, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Brief Communication
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

Internet

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

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