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Cell type discrimination based on image features of molecular component distribution
Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Ra...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079059/ https://www.ncbi.nlm.nih.gov/pubmed/30082723 http://dx.doi.org/10.1038/s41598-018-30276-1 |
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author | Germond, Arno Ichimura, Taro Chiu, Liang-da Fujita, Katsumasa Watanabe, Tomonobu M. Fujita, Hideaki |
author_facet | Germond, Arno Ichimura, Taro Chiu, Liang-da Fujita, Katsumasa Watanabe, Tomonobu M. Fujita, Hideaki |
author_sort | Germond, Arno |
collection | PubMed |
description | Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Raman spectral images and a method to extract features from these spatial patterns and explore the value of this information for cell discrimination. Raman images provide information regarding distribution of chemical compounds of the considered biological entity. Since each spectral wavelength can be used to reconstruct the distribution of a given compound, spectral images provide multiple channels of information, each representing a different pattern, in contrast to brightfield and fluorescence images. Using a dataset of single living cells, we demonstrate that the spatial information can be ranked by a Fisher discriminant score, and that the top-ranked features can accurately classify cell types. This method is compared with the conventional Raman spectral analysis. We also propose to combine the information from whole spectral analyses and selected spatial features and show that this yields higher classification accuracy. This method provides the basis for a novel and systematic analysis of cell-type investigation using Raman spectral imaging, which may benefit several studies and biomedical applications. |
format | Online Article Text |
id | pubmed-6079059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-60790592018-08-09 Cell type discrimination based on image features of molecular component distribution Germond, Arno Ichimura, Taro Chiu, Liang-da Fujita, Katsumasa Watanabe, Tomonobu M. Fujita, Hideaki Sci Rep Article Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Raman spectral images and a method to extract features from these spatial patterns and explore the value of this information for cell discrimination. Raman images provide information regarding distribution of chemical compounds of the considered biological entity. Since each spectral wavelength can be used to reconstruct the distribution of a given compound, spectral images provide multiple channels of information, each representing a different pattern, in contrast to brightfield and fluorescence images. Using a dataset of single living cells, we demonstrate that the spatial information can be ranked by a Fisher discriminant score, and that the top-ranked features can accurately classify cell types. This method is compared with the conventional Raman spectral analysis. We also propose to combine the information from whole spectral analyses and selected spatial features and show that this yields higher classification accuracy. This method provides the basis for a novel and systematic analysis of cell-type investigation using Raman spectral imaging, which may benefit several studies and biomedical applications. Nature Publishing Group UK 2018-08-06 /pmc/articles/PMC6079059/ /pubmed/30082723 http://dx.doi.org/10.1038/s41598-018-30276-1 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Germond, Arno Ichimura, Taro Chiu, Liang-da Fujita, Katsumasa Watanabe, Tomonobu M. Fujita, Hideaki Cell type discrimination based on image features of molecular component distribution |
title | Cell type discrimination based on image features of molecular component distribution |
title_full | Cell type discrimination based on image features of molecular component distribution |
title_fullStr | Cell type discrimination based on image features of molecular component distribution |
title_full_unstemmed | Cell type discrimination based on image features of molecular component distribution |
title_short | Cell type discrimination based on image features of molecular component distribution |
title_sort | cell type discrimination based on image features of molecular component distribution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079059/ https://www.ncbi.nlm.nih.gov/pubmed/30082723 http://dx.doi.org/10.1038/s41598-018-30276-1 |
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