Two‐Dimensional Light Scattering Anisotropy Cytometry for Label‐Free Classification of Ovarian Cancer Cells via Machine Learning | Zendy

Su Xuantao | Zendy; Yuan Tao | Zendy; Wang Zhiwen | Zendy; Song Kun | Zendy; Li Rongrong | Zendy; Yuan Cunzhong | Zendy; Kong Beihua | Zendy

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Two‐Dimensional Light Scattering Anisotropy Cytometry for Label‐Free Classification of Ovarian Cancer Cells via Machine Learning

Author(s) -

Su Xuantao,

Yuan Tao,

Wang Zhiwen,

Song Kun,

Li Rongrong,

Yuan Cunzhong,

Kong Beihua

Publication year - 2020

Publication title -

cytometry part a

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.316

H-Index - 90

eISSN - 1552-4930

pISSN - 1552-4922

DOI - 10.1002/cyto.a.23865

Subject(s) - anisotropy , cytometry , light scattering , artificial intelligence , flow cytometry , histogram , support vector machine , computer science , scattering , cancer cell , pattern recognition (psychology) , physics , cancer , biology , optics , microbiology and biotechnology , image (mathematics) , genetics

We develop a single‐mode fiber‐based cytometer for the obtaining of two‐dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label‐free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label‐free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry

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