Reliable and Efficient Tissue Segmentation in Whole-Slide Images

Whole-slide images in digital pathology often contain large regions of irrelevant background, making tissue segmentation an important preprocessing step in many applications. Traditional rule-based approaches to tissue segmentation often work quite well, but it is difficult to create general rules that cover all instances. We here apply an unmodified nnU-Net v2 training setup on downsampled whole-slide to develop and test an efficient and robust tissue segmentation model. The dataset contained nearly 30 000 images from slides with different tissue types, imaged using different scanners, and annotated using a semiautomatic workflow so that all annotations have been verified or made by human experts. This large, diverse dataset enables the training of a tissue segmentation model that generalizes well across different scanners and tissue types. We observed that our proposed model achieves similar or better accuracy than other deep learning models, while offering better robustness than simpler rule-based methods. The best compromise between inference speed and accuracy was observed using images at 10 µm per pixel. Our approach can be used as an efficient and well-suited preprocessing step for computational pathology. Source code, Dockerfiles, and model weights are made publicly available at: https://github. com/icgi/Reliable-and-Efficient-Tissue-Segmentation-in-Whole-Slide-Images.