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2 March 2022 Augmented quantitative phase imaging for large-scale label-free cancer-cell detection from heterogeneous tumors
Michelle C.K. Lo, Dickson M.D. Siu, Michael K. Y. Hsin, James C. M. Ho, Kevin K. Tsia
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Proceedings Volume PC11971, High-Speed Biomedical Imaging and Spectroscopy VII; PC119710D (2022) https://doi.org/10.1117/12.2608119
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
We present a quantitative phase image (QPI) reconstruction method using generative deep learning (with high similarity of 91% and low error rate of < 1%), and its ability to integrate with a high-throughput microfluidic multimodal imaging flow cytometry platform (called multi-ATOM) that can consistently classify cancer cells in heterogeneous tumors from human non-small cell lung cancer patients at large scale (~200,000 cells) and high accuracy (~98%); and can reveal biophysical heterogeneity of tumors. This work represents another groundwork of synergizing high-throughput QPI and deep learning for future label-free intelligent clinical cancer diagnosis.
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Michelle C.K. Lo, Dickson M.D. Siu, Michael K. Y. Hsin, James C. M. Ho, and Kevin K. Tsia "Augmented quantitative phase imaging for large-scale label-free cancer-cell detection from heterogeneous tumors", Proc. SPIE PC11971, High-Speed Biomedical Imaging and Spectroscopy VII, PC119710D (2 March 2022); https://doi.org/10.1117/12.2608119
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KEYWORDS
Tumors
Phase imaging
Lung cancer
Multiplexing
Network architectures
Optical instrument design
Optical microscopy
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