Quality control of whole-slide images through multi-class semantic segmentation of artifacts

G. Smit, F. Ciompi, M. Cigéhn, A. Bodén, J. van der Laak and C. Mercan

Medical Imaging with Deep Learning 2021.


Quality control is an integral part in the digitization process of whole-slide histopathology images due to artifacts that arise during various stages of slide preparation. Manual control and supervision of these gigapixel images are labor-intensive. Therefore, we report the first multi-class deep learning model trained on whole-slide images covering multiple tissue and stain types for semantic segmentation of artifacts. Our approach reaches a Dice score of 0.91, on average, across six artifact types, and outperforms the competition on external test set. Finally, we extend the artifact segmentation network to a multi-decision quality control system that can be deployed in routine clinical practice.