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Label-free classification of cells based on supervised machine learning of subcellular structures
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, whic...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350988/ https://www.ncbi.nlm.nih.gov/pubmed/30695059 http://dx.doi.org/10.1371/journal.pone.0211347 |
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author | 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 |
author_facet | 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 |
author_sort | Ozaki, Yusuke |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6350988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63509882019-02-15 Label-free classification of cells based on supervised machine learning of subcellular structures 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 PLoS One Research Article 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. Public Library of Science 2019-01-29 /pmc/articles/PMC6350988/ /pubmed/30695059 http://dx.doi.org/10.1371/journal.pone.0211347 Text en © 2019 Ozaki et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article 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 Label-free classification of cells based on supervised machine learning of subcellular structures |
title | Label-free classification of cells based on supervised machine learning of subcellular structures |
title_full | Label-free classification of cells based on supervised machine learning of subcellular structures |
title_fullStr | Label-free classification of cells based on supervised machine learning of subcellular structures |
title_full_unstemmed | Label-free classification of cells based on supervised machine learning of subcellular structures |
title_short | Label-free classification of cells based on supervised machine learning of subcellular structures |
title_sort | label-free classification of cells based on supervised machine learning of subcellular structures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350988/ https://www.ncbi.nlm.nih.gov/pubmed/30695059 http://dx.doi.org/10.1371/journal.pone.0211347 |
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