HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides - PubMed (original) (raw)

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

Andrew Janowczyk et al. JCO Clin Cancer Inform. 2019 Apr.

Abstract

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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Conflict of interest statement

Andrew R. Janowczyk

Consulting or Advisory Role: Merck

Hannah Gilmore

Travel, Accommodations, Expenses: Sectra

Michael Feldman

Consulting or Advisory Role: Philips Healthcare

Travel, Accommodations, Expenses: Philips Healthcare

Anant Madabhushi

Leadership: Inspirata

Stock and Other Ownership Interests: Inspirata, Elucid Bioimaging

Honoraria: AstraZeneca, Inspirata

Consulting or Advisory Role: Inspirata, AstraZeneca, Merck

Research Funding: Inspirata (Inst), Philips Healthcare (Inst)

Patents, Royalties, Other Intellectual Property: Intellectual property licensed by Inspirata (Inst); intellectual property licensed by Elucid Bioimaging (Inst)

No other potential conflicts of interest were reported.

Figures

FIG 1.

FIG 1.

Original images juxtaposed with corresponding results from HistoQC (fuchsia indicating acceptable tissue): images identified as having (A) a significant air bubble artefact requiring removal from experimental cohort, (B) blurry tissue near a coverslip crack, (C) folded tissue, and (D) pen markings correctly identified as regions to be avoided.

FIG 2.

FIG 2.

(A) HistoQC user interface showing table of HistoQC-produced metrics with sortable columns (green arrow), parallel coordinate plot (red box; additional details in Fig 3), and thumbnail images of the cohort alongside HistoQC overlay output indicating artefact-free regions (blue arrow). (B) Selecting a single image highlights the appropriate line in the parallel coordinate graph and shows the series of outputs produced by the modules of the pipeline, allowing for more detailed subsequent review. (C) Double clicking on any image brings up a higher-resolution version with dynamic zoom, allowing for fine-tuned inspection of potential artefacts. Hist, histogram; MPP, microns per pixel; MSE, mean squared error.

FIG 3.

FIG 3.

(A) Higher magnification of the parallel coordinate plot bounded by the red box in Figure 2. The different _y_-axes correspond to different image metrics determined by HistoQC and may have their own ranges and scales. Each horizontally oriented line, in either gray or blue, represents a whole-slide image (WSI) analyzed by HistoQC. By examining the convergence or divergence of each horizontal WSI line with respect to the rest of the images in the cohort, batch effects and outliers can be more easily visually identified. In the example illustrated, the user has interactively drawn a gray box (red arrow) to select images with a grayscale intensity value between 0.4 and 0.5, resulting in all lines that do not meet this criterion turning gray. This image selection mechanism alters the visibility of slides in both the table (Fig 2 green arrow) and the thumbnails (Fig 2 blue arrow). Furthermore, the user can dynamically drag or extend the gray box upward and downward to update the visible slides in real time. As a result, images with (B) low and (C) high grayscale brightness values are easier to identify and review.

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