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GrandQC: A comprehensive solution to quality control problem in digital pathology

Author

Listed:
  • Zhilong Weng

    (University Hospital Cologne)

  • Alexander Seper

    (Danube Private University)

  • Alexey Pryalukhin

    (University Hospital Wiener Neustadt / Danube Private University)

  • Fabian Mairinger

    (University Hospital Essen)

  • Claudia Wickenhauser

    (Martin Luther University Halle-Wittenberg)

  • Marcus Bauer

    (Martin Luther University Halle-Wittenberg)

  • Lennert Glamann

    (Martin Luther University Halle-Wittenberg)

  • Hendrik Bläker

    (University Hospital Leipzig)

  • Thomas Lingscheidt

    (University Hospital Leipzig)

  • Wolfgang Hulla

    (University Hospital Wiener Neustadt / Danube Private University)

  • Danny Jonigk

    (University Hospital Aachen
    Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH))

  • Simon Schallenberg

    (University Hospital Charite)

  • Andrey Bychkov

    (University Hospital Nagasaki
    Kameda Medical Center)

  • Junya Fukuoka

    (University Hospital Nagasaki
    Kameda Medical Center)

  • Martin Braun

    (MVZ Pathology and Cytology Rhein-Sieg)

  • Birgid Schömig-Markiefka

    (University Hospital Cologne)

  • Sebastian Klein

    (University Hospital Cologne)

  • Andreas Thiel

    (MVZ Pathology Bethesda)

  • Katarzyna Bozek

    (University of Cologne
    University of Cologne
    University of Cologne)

  • George J. Netto

    (Perelman School of Medicine at the University of Pennsylvania)

  • Alexander Quaas

    (University Hospital Cologne)

  • Reinhard Büttner

    (University Hospital Cologne)

  • Yuri Tolkach

    (University Hospital Cologne)

Abstract

Histological slides contain numerous artifacts that can significantly deteriorate the performance of image analysis algorithms. Here we develop the GrandQC tool for tissue and multi-class artifact segmentation. GrandQC allows for high-precision tissue segmentation (Dice score 0.957) and segmentation of tissue without artifacts (Dice score 0.919–0.938 dependent on magnification). Slides from 19 international pathology departments digitized with the most common scanning systems and from The Cancer Genome Atlas dataset were used to establish a QC benchmark, analyzing inter-institutional, intra-institutional, temporal, and inter-scanner slide quality variations. GrandQC improves the performance of downstream image analysis algorithms. We open-source the GrandQC tool, our large manually annotated test dataset, and all QC masks for the entire TCGA cohort to address the problem of QC in digital/computational pathology. GrandQC can be used as a tool to monitor sample preparation and scanning quality in pathology departments and help to track and eliminate major artifact sources.

Suggested Citation

  • Zhilong Weng & Alexander Seper & Alexey Pryalukhin & Fabian Mairinger & Claudia Wickenhauser & Marcus Bauer & Lennert Glamann & Hendrik Bläker & Thomas Lingscheidt & Wolfgang Hulla & Danny Jonigk & Si, 2024. "GrandQC: A comprehensive solution to quality control problem in digital pathology," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54769-y
    DOI: 10.1038/s41467-024-54769-y
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    References listed on IDEAS

    as
    1. Caglar Senaras & M Khalid Khan Niazi & Gerard Lozanski & Metin N Gurcan, 2018. "DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-13, October.
    2. Hanwen Xu & Naoto Usuyama & Jaspreet Bagga & Sheng Zhang & Rajesh Rao & Tristan Naumann & Cliff Wong & Zelalem Gero & Javier González & Yu Gu & Yanbo Xu & Mu Wei & Wenhui Wang & Shuming Ma & Furu Wei , 2024. "A whole-slide foundation model for digital pathology from real-world data," Nature, Nature, vol. 630(8015), pages 181-188, June.
    Full references (including those not matched with items on IDEAS)

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