Deep learning is widely applicable to phenotyping embryonic development and disease

Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalit...

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Detalles Bibliográficos
Autores principales: Naert, Thomas, Çiçek, Özgün, Ogar, Paulina, Bürgi, Max, Shaidani, Nikko-Ideen, Kaminski, Michael M., Xu, Yuxiao, Grand, Kelli, Vujanovic, Marko, Prata, Daniel, Hildebrandt, Friedhelm, Brox, Thomas, Ronneberger, Olaf, Voigt, Fabian F., Helmchen, Fritjof, Loffing, Johannes, Horb, Marko E., Willsey, Helen Rankin, Lienkamp, Soeren S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Company of Biologists Ltd 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602947/
https://www.ncbi.nlm.nih.gov/pubmed/34739029
http://dx.doi.org/10.1242/dev.199664

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602947/
https://www.ncbi.nlm.nih.gov/pubmed/34739029
http://dx.doi.org/10.1242/dev.199664

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