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Multi-class texture analysis in colorectal cancer histology
Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4910082/ https://www.ncbi.nlm.nih.gov/pubmed/27306927 http://dx.doi.org/10.1038/srep27988 |
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author | Kather, Jakob Nikolas Weis, Cleo-Aron Bianconi, Francesco Melchers, Susanne M. Schad, Lothar R. Gaiser, Timo Marx, Alexander Zöllner, Frank Gerrit |
author_facet | Kather, Jakob Nikolas Weis, Cleo-Aron Bianconi, Francesco Melchers, Susanne M. Schad, Lothar R. Gaiser, Timo Marx, Alexander Zöllner, Frank Gerrit |
author_sort | Kather, Jakob Nikolas |
collection | PubMed |
description | Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies. |
format | Online Article Text |
id | pubmed-4910082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-49100822016-06-16 Multi-class texture analysis in colorectal cancer histology Kather, Jakob Nikolas Weis, Cleo-Aron Bianconi, Francesco Melchers, Susanne M. Schad, Lothar R. Gaiser, Timo Marx, Alexander Zöllner, Frank Gerrit Sci Rep Article Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies. Nature Publishing Group 2016-06-16 /pmc/articles/PMC4910082/ /pubmed/27306927 http://dx.doi.org/10.1038/srep27988 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Kather, Jakob Nikolas Weis, Cleo-Aron Bianconi, Francesco Melchers, Susanne M. Schad, Lothar R. Gaiser, Timo Marx, Alexander Zöllner, Frank Gerrit Multi-class texture analysis in colorectal cancer histology |
title | Multi-class texture analysis in colorectal cancer histology |
title_full | Multi-class texture analysis in colorectal cancer histology |
title_fullStr | Multi-class texture analysis in colorectal cancer histology |
title_full_unstemmed | Multi-class texture analysis in colorectal cancer histology |
title_short | Multi-class texture analysis in colorectal cancer histology |
title_sort | multi-class texture analysis in colorectal cancer histology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4910082/ https://www.ncbi.nlm.nih.gov/pubmed/27306927 http://dx.doi.org/10.1038/srep27988 |
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