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An open access, machine learning pipeline for high-throughput quantification of cell morphology

Cell morphology is influenced by many factors and can be used as a phenotypic marker. Here we describe a machine-learning-based protocol for high-throughput morphological measurement of human fibroblasts using a standard fluorescence microscope and the pre-existing, open access software ilastik for...

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Detalles Bibliográficos
Autores principales:	Welter, Emma M., Kosyk, Oksana, Zannas, Anthony S.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Elsevier 2022
Materias:	Protocol
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792532/ https://www.ncbi.nlm.nih.gov/pubmed/36527712 http://dx.doi.org/10.1016/j.xpro.2022.101947

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