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.
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.