HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides
By Andrew Janowczyk, Ren Zuo, Hannah Gilmore, M. Feldman, Anant Madabhushi. JCO Clinical Cancer Informatics, 2019
325 citations recorded by the source corpus.
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 r-h 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 fora 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 darkeror 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.
Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/be2fd1571c6f993470d2d54dd9d9d7d07e4ef564
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Biophysics
In Computer Vision and Pattern Recognition
Citations in the corpus
- Digital pathology and artificial intelligence in translational medicine and clinical practice (2021, methodology): “Other approaches, such as combining image metrics in a QC application 138 , or transformation of image patches with synthetically generated artifacts 139 , can be used to train an algorithm to recognize different types of histological artifacts.”
- Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, methodology): “One way to mitigate this problem is to ensure that a robust quality-control procedure is in place, enabling the algorithm to detect unusual input data 129 .”
- Artificial intelligence in oncology (2020, methodology): “34 Some automated CNN-based tools, such as HistoQC and DeepFocus, 35,36 have been developed to standardize the quality of whole-slide imaging.”