B8 - Quality control of whole-slide images through multi-class semantic segmentation of artifacts

Gijs Smit, Francesco Ciompi, Maria Cigéhn, Anna Bodén, Jeroen van der Laak, Caner Mercan

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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.
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Wednesday 7th July
B1-9 (short): Application: Histopathology - 13:45 - 14:30 (UTC+2)
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