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

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.

Bio: Z. Weng is a PhD student at the University of Cologne and the University Hospital Cologne, supervised by Yuri Tolkach. He got his master’s degree in Computational Engineering from the Technical University of Darmstadt in Germany, where he focused on computer vision research, including traditional image processing and deep learning-based image detection. His current research focuses on advancing artificial intelligence applications in computational pathology.

Paper Link: GrandQC: A comprehensive solution to quality control problem in digital pathology | Nature CommunicationsLink opens in a new window

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