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

Zhilong Weng, Alexander Seper, Alexey Pryalukhin, Fabian Mairinger, Claudia Wickenhauser, Marcus Bauer, Lennert Glamann, Hendrik Bläker, Thomas Lingscheidt, Wolfgang Hulla, Danny Jonigk, Simon Schallenberg, Andrey Bychkov, Junya Fukuoka, Martin Braun, Birgid Schömig-Markiefka, Sebastian Klein, Andreas Thiel, Katarzyna Bozek, George J. Netto, Alexander Quaas, Reinhard Büttner and Yuri Tolkach ()
Additional contact information
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
Simon Schallenberg: University Hospital Charite
Andrey Bychkov: University Hospital Nagasaki
Junya Fukuoka: University Hospital Nagasaki
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
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

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-54769-y

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