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Rapid assessment of the viability of E. coli and other bacteria pathogens is important for timely monitoring of water quality. Therefore, we propose a label-free method for assessing the viability of E. coli cells in a fast way by using quantitative phase microscopy (QPM) and machine learning. According to the viability levels, E. coli cell populations were divided into two classes that were treated with 0.9% and 25% sodium chloride (NaCl) suspended in phosphate-buffered saline (PBS) solution, respectively. Their high contrast phase images are acquired by a high sensitivity diffraction phase microscope. To determine the viability class of individual E. coli cells, a residual neural network (ResNet) is developed to extract the rich information contained in the phase images. An average testing accuracy as high as 95.5% has been achieved in predicting the two viability classes.
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Yujie Nie, Xin Shu, Renjie Zhou, "Label-free analysis of E. coli viability using quantitative phase imaging and machine learning," Proc. SPIE 11970, Quantitative Phase Imaging VIII, 119700G (2 March 2022); https://doi.org/10.1117/12.2609934