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HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.
Andrew Janowczyk1, Ren Zuo1, Hannah Gilmore2
1Case Western Reserve University, Cleveland, OH.
JCO Clinical Cancer Informatics
|April 17, 2019
Summary
HistoQC is an open-source tool for automated quality control in digital pathology. It identifies artefacts and outliers, improving the reliability of computational analysis on digitized slides.
Area of Science:
- Computational pathology
- Digital pathology image analysis
- Medical image processing
Background:
- Digital pathology (DP) is transforming clinical diagnostics, but artefacts and batch effects pose challenges.
- Manual slide review is time-consuming, subjective, and prone to variability.
- Automated artefact detection is crucial for reproducible DP workflows.
Purpose of the Study:
- To introduce HistoQC, an open-source tool for automated quality control in digital pathology.
- To identify and delineate artefacts and cohort-level outliers in digitized slides.
- To enable reproducible and robust computational analysis of digital slides.
Main Methods:
- HistoQC utilizes image metrics, features, and supervised classifiers for artefact detection.
- It identifies artefact-free regions and provides real-time visualization via a GUI.
- Users can define acceptable quality tolerances for their specific workflows.
Main Results:
- HistoQC was evaluated on 450 slides from The Cancer Genome Atlas.
- Pathologist review confirmed that over 95% of slides were suitable for computational analysis.
- The tool effectively identifies artefacts and cohort-level variations.
Conclusions:
- HistoQC offers an automated, quantifiable quality control process for digital pathology.
- It enhances the repeatability and robustness of digital pathology workflows.
- The tool aids in identifying slides or regions requiring re-processing or exclusion from analysis.
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