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10 October 2020 High-content 2D light scattering flow cytometry for label-free classification of cervical carcinoma cells with deep learning
Chao Liu, Qiao Liu, Xuantao Su
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Proceedings Volume 11553, Optics in Health Care and Biomedical Optics X; 115532J (2020) https://doi.org/10.1117/12.2575099
Event: SPIE/COS Photonics Asia, 2020, Online Only
Conference Poster
Abstract
Two-dimensional (2D) light scattering has the capability for label-free single cell analysis. Recent development of flow cytometry has demonstrated the obtaining of high-content images. Here we demonstrate a flow cytometer for the obtaining of high-content 2D light scattering patterns of single cells. In our flow cytometer, single cells are flowing in a hydrodynamic focusing unit and their 2D light scattering patterns are recorded via a long working distance objective by using a high-speed complementary metal oxide semiconductor (CMOS) sensor. Big data of the 2D light scattering patterns from two types of cervical carcinoma cell lineage cells (HeLa and C33-A) are obtained with a rate of 60 frames per second. Deep learning is adopted for the classification of these two types of cells, and a high recognition accuracy is obtained. The results show that our high-content 2D light scattering flow cytometry together with deep learning can collect label-free single-cell information at high speed and has strong analytical capabilities, which may in future be used for early diagnosis of cervical carcinoma.
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Chao Liu, Qiao Liu, and Xuantao Su "High-content 2D light scattering flow cytometry for label-free classification of cervical carcinoma cells with deep learning", Proc. SPIE 11553, Optics in Health Care and Biomedical Optics X, 115532J (10 October 2020); https://doi.org/10.1117/12.2575099
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KEYWORDS
Light scattering
Flow cytometry
CMOS sensors
CMOS technology
Sensors
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