XFibrosis: Explicit Vessel-Fiber Modeling for Fibrosis Staging from Liver Pathology Images

Chong Yin, Siqi Liu, Fei Lyu, Jiahao Lu, Sune Darkner, Vincent Wai-Sun Wong, Pong C. Yuen

Abstract: The increasing prevalence of non-alcoholic fatty liver disease (NAFLD) has caused public concern in recent years. The high prevalence and risk of severe complications make monitoring NAFLD progression a public health priority. Fibrosis staging from liver biopsy images plays a key role in demonstrating the histological progression of NAFLD. Fibrosis mainly involves the deposition of fibers around vessels. Current deep learning-based fi-brosis staging methods learn spatial relationships between tissue patches but do not explicitly consider the relation-ships between vessels and fibers, leading to limited performance and poor interpretability. In this paper, we propose an eXplicit vessel-fiber modeling method for Fibrosis staging from liver biopsy images, namely XFibrosis. Specifically, we transform vessels and fibers into graph-structured representations, where their micro-structures are depicted by vessel-induced primal graphs andfiber-induced dual graphs, respectively. Moreover, the fiber-induced dual graphs also represent the connectivity information between vessels caused by fiber deposition. A primal-dual graph convolution module is designed to facilitate the learning of spatial relationships between vessels and fibers, allowing for the joint exploration and interaction of their micro-structures. Experiments conducted on two datasets have shown that explicitly modeling the relationship between vessels and fibers leads to improved fibrosis staging and en-hanced interpretability.