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Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.

ABSTRACT: In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

SUBMITTER: Belashov AV 

PROVIDER: S-EPMC8533984 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.

Belashov Andrey V AV   Zhikhoreva Anna A AA   Belyaeva Tatiana N TN   Salova Anna V AV   Kornilova Elena S ES   Semenova Irina V IV   Vasyutinskii Oleg S OS  

Cells 20210929 10

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algor  ...[more]

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