Automatic Image Quality Assessment for Digital Pathology

Avanaki, Ali R. N.; Espig, Kathryn S.; Xthona, Albert; Lanciault, Christian; Kimpe, Tom

doi:10.1007/978-3-319-41546-8_54

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…Building further on the need to incorporate additional features, we envision HistoQC evolving into a collection of community-driven reference implementations of sophisticated detectors and metrics. For example, Senaras et al 22 presented a deep learning-based blur detector, and Avanaki et al 19 proposed texture-based image quality metrics. We hope that these types of algorithms will in the future be embedded into HistoQC to enable the comparison of results across different sites and laboratories.…”

Section: Discussionmentioning

confidence: 99%

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HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

Janowczyk

Zuo

Gilmore

et al. 2019

JCO Clinical Cancer Informatics

241

175

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PURPOSE Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis. METHODS Here we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows. RESULTS The output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time. CONCLUSION These results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.

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Section: Discussionmentioning

confidence: 99%

“…Recently, other groups 19–23 have begun to develop DP algorithms for QC tasks, such as blurriness and stain assessment. Unfortunately, there has not been a single, unified user-friendly platform that has included these and other QC approaches for a comprehensive and integrated QC review of DP slide images.…”

Section: Introductionmentioning

confidence: 99%

HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

Janowczyk

Zuo

Gilmore

et al. 2019

JCO Clinical Cancer Informatics

241

175

View full text Add to dashboard Cite

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“…Thus, during a similar transition in pathology, digital QC tools are expected to be similarly adopted. To this end, there have already been efforts to develop algorithms to improve QC in the DPR space, such as those detecting blurriness [16] and assessing slide quality [17–19]. HistoQC is not designed to replace these methods, but instead to provide a singular open‐sourced pipeline for them to be embedded in, thus providing a means to visually examine their outputs via a singular user interface.…”

Section: Discussionmentioning

confidence: 99%

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

Chen

Zee

Smith

et al. 2021

The Journal of Pathology

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Inconsistencies in the preparation of histology slides and whole‐slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e. systematic technical artifacts unrelated to biological variability) may introduce biases to machine learning algorithms. To date, manual quality control (QC) has been the de facto standard for dataset curation, but remains highly subjective and is too laborious in light of the increasing scale of tissue slide digitization efforts. This study aimed to evaluate a computer‐aided QC pipeline for facilitating a reproducible QC process of WSI datasets. An open source tool, HistoQC, was employed to identify image artifacts and compute quantitative metrics describing visual attributes of WSIs to the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository. A comparison in inter‐reader concordance between HistoQC aided and unaided curation was performed to quantify improvements in curation reproducibility. HistoQC metrics were additionally employed to quantify the presence of batch effects within NEPTUNE WSIs. Of the 1814 WSIs (458 H&E, 470 PAS, 438 silver, 448 trichrome) from n = 512 cases considered in this study, approximately 9% (163) were identified as unsuitable for subsequent computational analysis. The concordance in the identification of these WSIs among computational pathologists rose from moderate (Gwet's AC1 range 0.43 to 0.59 across stains) to excellent (Gwet's AC1 range 0.79 to 0.93 across stains) agreement when aided by HistoQC. Furthermore, statistically significant batch effects (p < 0.001) in the NEPTUNE WSI dataset were discovered. Taken together, our findings strongly suggest that quantitative QC is a necessary step in the curation of digital pathology cohorts. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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“…In the category of low-level digital image processing, there have been several simple coarse-grain slide quality assessment works [20] [21]. However, the disadvantages of these works are quite obvious: 1.…”

Section: Automation In Microscopic Medical Image Analysismentioning

confidence: 99%

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

Zhang

Carvajal

Smith

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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This work tackles the automatic fine-grained slide quality assessment problem for digitized direct smears test using the Gram staining protocol. Automatic quality assessment can provide useful information for the pathologists and the whole digital pathology workflow. For instance, if the system found a slide to have a low staining quality, it could send a request to the automatic slide preparation system to remake the slide. If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required. In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way. Specifically, the first step of our framework is to perform dense fine-grained region classification on the whole slide and calculate the region distribution histogram. Next, our framework will generate assessments of the slide quality from various perspectives: staining quality, information density, damage level and which regions are more valuable for subsequent high-magnification analysis. To make the information more accessible, we present our results in the form of a heat map and text summaries. Additionally, in order to stimulate research in this direction, we propose a novel dataset for slide quality assessment. Experiments show that the proposed framework outperforms recent related works.

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Automatic Image Quality Assessment for Digital Pathology

Cited by 12 publications

References 14 publications

HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

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