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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...

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Autores principales: Kather, Jakob Nikolas, Weis, Cleo-Aron, Bianconi, Francesco, Melchers, Susanne M., Schad, Lothar R., Gaiser, Timo, Marx, Alexander, Zöllner, Frank Gerrit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
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.
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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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