Abstract
We demonstrate that live-dead cell assay can be conducted in a label-free manner using quantitative phase imaging and deep learning. We apply the concept of our newly-developed phase imaging with computational specificity (PICS) to digitally stain for the live/dead markers. HeLa cultured mixed with viability fluorescent reagents (ReadyProbes, ThermoFisher) were imaged for 24 hours by spatial light interference microscopy (SLIM) and fluorescent microscopy. Based on the ratio of the two fluorescence signals, semantic segmentation maps were generated to label the state of the cell as either live, injured, or dead. We trained an EfficientNet to infer cell viability from SLIM images with semantic maps as ground truth. Validated on the testing dataset, the trained network reported an F1 score of 73.4%, 97.0%, and 94.3% in identifying live, injured, and dead cells, respectively.
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