An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides

Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcatio...

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Main Authors:	Zarella, Mark D., Breen, David E., Plagov, Andrei, Garcia, Fernando U.
Format:	Online
Language:	English
Published:	Medknow Publications & Media Pvt Ltd 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4485192/

id	pubmed-4485192
recordtype	oai_dc
spelling	pubmed-44851922015-07-12 An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides Zarella, Mark D. Breen, David E. Plagov, Andrei Garcia, Fernando U. Research Article Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing. Medknow Publications & Media Pvt Ltd 2015-06-23 /pmc/articles/PMC4485192/ /pubmed/26167377 http://dx.doi.org/10.4103/2153-3539.158910 Text en Copyright: © 2015 Zarella MD. http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zarella, Mark D. Breen, David E. Plagov, Andrei Garcia, Fernando U.
spellingShingle	Zarella, Mark D. Breen, David E. Plagov, Andrei Garcia, Fernando U. An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
author_facet	Zarella, Mark D. Breen, David E. Plagov, Andrei Garcia, Fernando U.
author_sort	Zarella, Mark D.
title	An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
title_short	An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
title_full	An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
title_fullStr	An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
title_full_unstemmed	An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
title_sort	optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
description	Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing.
publisher	Medknow Publications & Media Pvt Ltd
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4485192/
_version_	1613241620278280192

An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides

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