GrandQC: A comprehensive solution to quality control problem in digital pathology
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.
Authors
- M. Braun (ORCID: https://orcid.org/0000-0003-2419-7816)
- Reinhard Büttner (ORCID: https://orcid.org/0000-0001-8806-4786)
- Alexander Quaas (ORCID: https://orcid.org/0000-0002-9644-2115)
- Claudia Wickenhauser (ORCID: https://orcid.org/0000-0002-1807-5575)
- Birgid Schömig‐Markiefka (ORCID: https://orcid.org/0000-0003-1893-8796)
- Hendrik Bläker (ORCID: https://orcid.org/0000-0002-2497-8172)
- Fabian Mairinger (ORCID: https://orcid.org/0000-0003-1067-0051)
- Junya Fukuoka (ORCID: https://orcid.org/0000-0002-2496-3050)
- Andrey Bychkov (ORCID: https://orcid.org/0000-0002-4203-5696)
- Andreas Thiel (ORCID: https://orcid.org/0000-0002-5515-4002)
- Simon Schallenberg (ORCID: https://orcid.org/0000-0002-7897-7116)
- Wolfgang Hulla (ORCID: https://orcid.org/0000-0001-8189-4621)
- Yuri Tolkach (ORCID: https://orcid.org/0000-0001-5239-2841)
- Marcus Bauer (ORCID: https://orcid.org/0000-0002-0657-6831)
- George J. Netto (ORCID: https://orcid.org/0000-0003-3915-9134)
- Sebastian Klein (ORCID: https://orcid.org/0000-0002-2188-9377)
- Danny Jonigk (ORCID: https://orcid.org/0000-0002-5251-2281)
- Katarzyna Bożek (ORCID: https://orcid.org/0000-0002-0917-6876)
- Thomas Lingscheidt
- Ziming Weng (ORCID: https://orcid.org/0009-0002-0321-9406)
- Alexander Seper
- Alexey Pryalukhin
- Lennert Glamann
Institutions
- Danube Private University (AT)
- University of Cologne (DE)
- Fachhochschule Wiener Neustadt (AT)
- Kameda Medical Center (JP)
- Essen University Hospital (DE)
- Universitätsklinikum Aachen (DE)
- University Hospital Leipzig (DE)
- German Center for Lung Research (DE)
- Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE)
- University Hospital Cologne (DE)
- Nagasaki University Hospital (JP)
- Martin Luther University Halle-Wittenberg (DE)
- Charité - Universitätsmedizin Berlin (DE)
- University of Pennsylvania (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2024-12-16
- DOI
- https://doi.org/10.1038/s41467-024-54769-y
- Citations
- 36
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 12.51
Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Universität zu Köln