CPU-READY DEEP LEARNING APPROACH FOR ROBUST TISSUE REGION SEGMENTATION ACROSS MULTI-COHORT H&E AND IHC-STAINED WHOLE SLIDE IMAGES

With the rise of digital pathology, integrating digital slides with deep learning–based decision support systems is becoming increasingly common in clinical practice. Tissue region segmentation which is distinguishing tissue from background/artefacts, is an important pre-requisite in many digital pathology pipelines both for the laboratories as their first step in digitalizing the glass slides of tissue samples and turning them to whole slide images (WSIs) using scanners, and also for DL researches such as region-of-interest cropping, tumor detection, cell segmentation. However, it is well known that WSI scanners can fail in detecting all tissue regions, due to the tissue type, or due to weak staining and this is because of their not robust enough tissue detection algorithms which makes segmentation of WSIs a challenging task. Hence, this study develops a fast, lightweight, accurate, CPU-ready DL approach, enabling fast and reliable tissue region segmentation model by training and testing it across seven different institutional H&E and IHC stained WSIs to result a strong in generalization with the 22 to 56 s/WSI inference time using CPU that markedly outperforms classical OTSU thresholding, particularly in preserving challenging or faint tissue regions by achieving notably higher and more consistent performance than OTSU, with median Jaccard and Dice scores of approximately 0.86 and 0.92, respectively, compared to OTSU whcih was between 0.56 and 0.72. Our approach provides a practical, open-source solution for resource-limited pathology settings. We publicly released dataset obtained from Bahcesehir Medical School, and code to foster benchmarking and further advances in efficient, deployable computational pathology. The model could be used in digital slide scanners to improve the scanning process and in the pre-processing stages of DL pipelines to prepare high-quality datasets.