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...

Descripción completa

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

Internet

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

Ejemplares similares