Label-free analysis of E. coli viability using quantitative phase imaging and machine learning

Yujie Nie; Xin Shu; Renjie Zhou

doi:10.1117/12.2609934

2 March 2022 Label-free analysis of E. coli viability using quantitative phase imaging and machine learning

Yujie Nie, Xin Shu, Renjie Zhou

Proceedings Volume 11970, Quantitative Phase Imaging VIII; 119700G (2022) https://doi.org/10.1117/12.2609934
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Yujie Nie, Xin Shu, and 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

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