Impact of scanner variability on lymph node segmentation in computational pathology

Khan, Amjad; Janowczyk, Andrew; Müller, F.; Blank, Annika; Nguyen, Huu Giao; Abbet, Christian; Studer, Linda; Lugli, Alessandro; Dawson, Heather; Thiran, Jean‐Philippe; Zlobec, Inti

doi:10.1016/j.jpi.2022.100127

Cited by 10 publications

(12 citation statements)

References 36 publications

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“…In addition to variation across hospitals, the various hardware technology (e.g., scanners) that form the image procurement pipeline can also yield variation in predictive performance. Reference 249 evaluates the effect of this heterogeneity on the task of lymph node segmentation, and show that slide color normalization, model fine‐tuning, and domain adversarial learning are promising means of accounting for such discrepancies.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Machine learning in computational histopathology: Challenges and opportunities

Cooper

Krishnan

2023

Genes Chromosomes & Cancer

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Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

Machine learning in computational histopathology: Challenges and opportunities

Cooper

Krishnan

2023

Genes Chromosomes & Cancer

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“…There are a number of studies that show the negative impact of color variation in WSIs on the performance of AI and automated analysis, and demonstrate the need to mitigate the variation. [1][2][3][4][5][6][7][11][12][13][14] We mentioned three inconsistencies in the whole slide imaging system that induce color variation in WSIs, and in this article, we focused on addressing the color variation among different WSI scanners using a color calibration slide. To further improve the color homogeneity in WSIs for digital pathology applications, integrating stain normalization into the process is the next step to resolve most of the evident inconsistencies in the system.…”

Section: Author Contributionsmentioning

confidence: 99%

“…Inconsistent optical and hardware characteristics of the WSI scanners can induce distorted color transformation from the tissue samples to the WSIs and inter-scanner color variation in the images. [1][2][3][4][5][6] 3. Distorted color rendering from display devices can result in inconsistent color presentation to the viewer.…”

Section: Introductionmentioning

confidence: 99%

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

Chu,

Ge,

Eastham

et al. 2023

Toxicol Pathol

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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.

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“… 7 This represents a problem since a minor variation in the final WSI, which represents no problem for the human eye, can be enough to significantly affect the performance of AI/CAD tools. 8 , 9 As the histological laboratories are increasingly automated and the procedures become standardized, a significative caveat that still burden the routine diagnostic workup is represented by slide stain quality. Indeed, several factors can affect the final appearance of slides staining, resulting in color and intensity variation in the histopathological images.…”

Section: Introductionmentioning

confidence: 99%