Fiber HGNN: Heterogeneous Graph Neural Network for Fiber Tract Segmentation

Fiber tract segmentation is crucial for clinical applications such as brain function interpretation and surgical planning. Existing methods typically adopt either a cortical-parcellation-based or fiber clustering approach, but fail to simultaneously integrate heterogeneous information (e.g., streamline shape, point position, anatomical priors). In this work, we propose Fiber HGNN, a novel heterogeneous graph neural network that explicitly models and integrates heterogeneous information of fibers for accurate fiber tract segmentation. We construct a heterogeneous graph comprising three types of nodes: streamline, fiber keypoint and anatomical region. Specifically, fiber keypoints are representative points sampled along each streamline to characterize local geometric features, while anatomical regions provide contextual priors derived from brain atlas. This design enables the network to jointly capture the complementary information of streamline shape, local geometry, and anatomical priors, thus facilitating the learning of more discriminative feature representations. To further leverage implicit anatomical connectivity, we design a Metapath-guided Heterogeneous Information Aggregation (MHIA) network. By analyzing the spatial relationships between streamline keypoints and anatomical regions, the heterogeneous graph is decomposed into anatomical subgraphs for each streamline. In each subgraph, heterogeneous information from metapath-linked nodes is aggregated to obtain the final fiber representation. We evaluate the effectiveness of our framework on the HCP105 and TractoInferno datasets. The experimental results demonstrate that our method significantly outperforms previous state-of-the-art methods. The source code is available at https://github.com/CUHK-AIM-Group/Fiber-HGNN.