DynaMorph: self-supervised learning of morphodynamic states of live cells.
Ontology highlight
ABSTRACT:
SUBMITTER: Wu Z
PROVIDER: S-EPMC9265147 | biostudies-literature | 2022 May
REPOSITORIES: biostudies-literature
Publications
DynaMorph: self-supervised learning of morphodynamic states of live cells.
Wu Zhenqin Z Chhun Bryant B BB Popova Galina G Guo Syuan-Ming SM Kim Chang N CN Yeh Li-Hao LH Nowakowski Tomasz T Zou James J Mehta Shalin B SB
Molecular biology of the cell 20220209 6
A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph-a computational framework that combines quantitative live cell imaging with self-supervised learning. To ...[more]
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