GAGM: Geometry-aware graph matching framework for weakly supervised gyral hinge correspondence

Abstract

Achieving precise alignment of inter-subject brain landmarks, such as the gyral hinge (GH), would enhance the correspondence of brain function across subjects, thereby advancing our understanding of brain anatomy-function relationship and brain mechanisms. Recent methods mainly focus on identifying the correspondences of GHs by utilizing point-to-point ground truth. However, labeling point-to-point GH correspondences between subjects for the entire brain is laborious and time-consuming, given the presence of over 400 GHs per brain. To remedy this problem, we propose a Geometry-Aware Graph Matching framework, dubbed GAGM, for weakly supervised gyral hinge correspondence solely based on brain prior information. Specifically, we propose a Shape-Aware Graph Establishment (SAGE) module to ensure a comprehensive representation of geometry features in GH. SAGE constructs a structured graph by incorporating GH coordinates, shapes, and inter-GH relationships to model entire brain GHs and learns the spatial relation between them. Moreover, to reduce the optimization difficulties, Region-Aware Graph Matching (RAGM) module is proposed for multi-scale matching. RAGM leverages prior knowledge of the multi-scale relationship between GHs and brain regions and incorporates inter-scale semantic consistency to ensure both intra-region consistency and inter-region variability of GH features, ultimately achieving accurate GH matching. Extensive experiments on two public datasets, HCP and CHCP, demonstrate the superiority of our method over state-of-the-art methods. Our code: https://github.com/ZhibinHe/GAGM.