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Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning.
Su, Xuantao; Yuan, Tao; Wang, Zhiwen; Song, Kun; Li, Rongrong; Yuan, Cunzhong; Kong, Beihua.
Affiliation
  • Su X; Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Yuan T; Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Wang Z; Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Song K; Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.
  • Li R; Gynecology Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan, 250012, China.
  • Yuan C; Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.
  • Kong B; Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.
Cytometry A ; 97(1): 24-30, 2020 01.
Article in En | MEDLINE | ID: mdl-31313517
ABSTRACT
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ovarian Neoplasms / Anisotropy / Flow Cytometry / Machine Learning Type of study: Diagnostic_studies Limits: Female / Humans Language: En Journal: Cytometry A Year: 2020 Type: Article Affiliation country: China
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ovarian Neoplasms / Anisotropy / Flow Cytometry / Machine Learning Type of study: Diagnostic_studies Limits: Female / Humans Language: En Journal: Cytometry A Year: 2020 Type: Article Affiliation country: China