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Label-free classification of cells based on supervised machine learning of subcellular structures.
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- Author(s): Ozaki, Yusuke; Yamada, Hidenao; Kikuchi, Hirotoshi; Hirotsu, Amane; Murakami, Tomohiro; Matsumoto, Tomohiro; Kawabata, Toshiki; Hiramatsu, Yoshihiro; Kamiya, Kinji; Yamauchi, Toyohiko; Goto, Kentaro; Ueda, Yukio; Okazaki, Shigetoshi; Kitagawa, Masatoshi; Takeuchi, Hiroya; Konno, Hiroyuki
- Source:
PLoS ONE; 1/29/2019, Vol. 14 Issue 1, p1-20, 20p- Subject Terms:
- Source:
- Additional Information
- Abstract: It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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