Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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
_version_ 1783278302857789440
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
work_keys_str_mv AT homeyerandre automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT nasrpatrik automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT engelchristiane automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT kechagiasstergios automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT lundbergpeter automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT ekstedtmattias automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT kosthenning automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT weissnick automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT palmertim automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT hahnhorstkarl automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT treanordarren automatedquantificationofsteatosisagreementwithstereologicalpointcounting
AT lundstromclaes automatedquantificationofsteatosisagreementwithstereologicalpointcounting