Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology.

Autor:	Chu ML; Genentech Inc., South San Francisco, California, USA., Ge XM; Genentech Inc., South San Francisco, California, USA., Eastham J; Genentech Inc., South San Francisco, California, USA., Nguyen T; Genentech Inc., South San Francisco, California, USA., Fuji RN; Genentech Inc., South San Francisco, California, USA., Sullivan R; Genentech Inc., South San Francisco, California, USA., Ruderman D; Genentech Inc., South San Francisco, California, USA.
Jazyk:	angličtina
Zdroj:	Toxicologic pathology [Toxicol Pathol] 2023 Aug; Vol. 51 (6), pp. 313-328. Date of Electronic Publication: 2024 Jan 30.
DOI:	10.1177/01926233231224468
Abstrakt:	Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Mei-Lan Chu, Xing-Yue M Ge, Jeffrey Eastham, Trung Nguyen, Reina N Fuji, Ruth Sullivan and Daniel Ruderman are employees of Genentech, Inc. and are Roche stockholders.
Databáze:	MEDLINE
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