Machine-learning-mediated single-cell classification by hyperspectral stimulated Raman scattering imaging

Tianrun Chen; Qingyue Cheng; Hyeon Jeong Lee

doi:10.1117/12.2602857

9 October 2021 Machine-learning-mediated single-cell classification by hyperspectral stimulated Raman scattering imaging

Tianrun Chen, Qingyue Cheng, Hyeon Jeong Lee

Author Affiliations +

Proceedings Volume 11900, Optics in Health Care and Biomedical Optics XI; 119000V (2021) https://doi.org/10.1117/12.2602857
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Abstract

Cell classification is a fundamental task in biological research and medical practices. In this study, we proposed a singlecell classification pipeline through machine learning and hyperspectral stimulated Raman scattering imaging. The pipeline proposed is validated by using hyperspectral SRS images of two types of pancreatic cancer cells before and after the treatment of drugs that affects cellular cholesterol level. The result demonstrates that the proposed machine learning pipeline is capable of classifying cells with different metabolite dynamics, which provides possibilities for wide applications in cell analysis.

Citation Download Citation

Tianrun Chen, Qingyue Cheng, and Hyeon Jeong Lee "Machine-learning-mediated single-cell classification by hyperspectral stimulated Raman scattering imaging", Proc. SPIE 11900, Optics in Health Care and Biomedical Optics XI, 119000V (9 October 2021); https://doi.org/10.1117/12.2602857

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