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Automated quantification of steatosis: agreement with stereological point counting
BACKGROUND: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with s...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683532/ https://www.ncbi.nlm.nih.gov/pubmed/29132399 http://dx.doi.org/10.1186/s13000-017-0671-y |
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author | Homeyer, André Nasr, Patrik Engel, Christiane Kechagias, Stergios Lundberg, Peter Ekstedt, Mattias Kost, Henning Weiss, Nick Palmer, Tim Hahn, Horst Karl Treanor, Darren Lundström, Claes |
author_facet | Homeyer, André Nasr, Patrik Engel, Christiane Kechagias, Stergios Lundberg, Peter Ekstedt, Mattias Kost, Henning Weiss, Nick Palmer, Tim Hahn, Horst Karl Treanor, Darren Lundström, Claes |
author_sort | Homeyer, André |
collection | PubMed |
description | BACKGROUND: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist. METHODS: The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability. RESULTS: The new method showed the strongest agreement with the expert. At 20× resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10× resolution, it was more accurate than and twice as fast as all other methods at 20× resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer. CONCLUSIONS: The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers. |
format | Online Article Text |
id | pubmed-5683532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56835322017-11-20 Automated quantification of steatosis: agreement with stereological point counting Homeyer, André Nasr, Patrik Engel, Christiane Kechagias, Stergios Lundberg, Peter Ekstedt, Mattias Kost, Henning Weiss, Nick Palmer, Tim Hahn, Horst Karl Treanor, Darren Lundström, Claes Diagn Pathol Research BACKGROUND: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist. METHODS: The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability. RESULTS: The new method showed the strongest agreement with the expert. At 20× resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10× resolution, it was more accurate than and twice as fast as all other methods at 20× resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer. CONCLUSIONS: The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers. BioMed Central 2017-11-13 /pmc/articles/PMC5683532/ /pubmed/29132399 http://dx.doi.org/10.1186/s13000-017-0671-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Homeyer, André Nasr, Patrik Engel, Christiane Kechagias, Stergios Lundberg, Peter Ekstedt, Mattias Kost, Henning Weiss, Nick Palmer, Tim Hahn, Horst Karl Treanor, Darren Lundström, Claes Automated quantification of steatosis: agreement with stereological point counting |
title | Automated quantification of steatosis: agreement with stereological point counting |
title_full | Automated quantification of steatosis: agreement with stereological point counting |
title_fullStr | Automated quantification of steatosis: agreement with stereological point counting |
title_full_unstemmed | Automated quantification of steatosis: agreement with stereological point counting |
title_short | Automated quantification of steatosis: agreement with stereological point counting |
title_sort | automated quantification of steatosis: agreement with stereological point counting |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683532/ https://www.ncbi.nlm.nih.gov/pubmed/29132399 http://dx.doi.org/10.1186/s13000-017-0671-y |
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