Article
Deep learning is widely applicable to phenotyping embryonic development and disease.
Development (Cambridge, England) - 1 Nov 2021
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
Abstract excerpt
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 modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in...
Read the complete abstract on PubMedTopics
Share this publication in a Topic to start or enrich a Post.