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

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.

Automated summary

This report presents the implementation and validation of machine-learning classifiers for distinguishing between different cell types and states based on the analysis of optical parameters derived from cell phase images. The developed classifier shows high accuracy in distinguishing both cell types and states, and successfully evaluates the temporal dynamics of cell states after photodynamic treatment.