Group affinity weakly supervised segmentation from prior selected tissue in colorectal histopathology images

Precise tissue segmentation of histopathology images is often a crucial step in computational pathology pipelines. However, visual scoring by pathologists is sensitive and depends on their experience and perception. Therefore, there is a need for novel automatic systems to improve the accuracy and reproducibility of pathologists' interpretations. Here, a group affinity weakly supervised segmentation method (GAWS) is proposed to conquer this task, with the following pipeline. First, we create a cluster image by extracting the visual feature of each pixel using CNN and clustering it into different classes. Then, we create a target image by refining this cluster image with the constraints on prior tissue, color, and spatial distribution of pixels. Finally, a backpropagation process with a segmentation loss is considered to evaluate the error signals between cluster and target images and update the network parameters. We validate our method with extracellular mucin-to-tumor area quantification using a colorectal cancer clinical dataset with 163 Hematoxylin Eosin (H&E) whole slide images from 97 patients. Inter-observer agreement between pathologists and the proposed algorithm is excellent (ICC=0.917) and more accurate compared with two state-of-the-art unsupervised segmentation methods. Our results show that the GAWS results in a high average performance and excellent reliability when applied to histopathology images and possibly is a promising method for inclusion into clinical practice. This approach takes advantage of weakly supervised learning without any pre-trained network to have a tumor quantification tool that could improve the pathologist's workflow.