3DABSeg: Adaptive 3D Ankle Bone Segmentation with Multiscale Feature Fusion Mixture-of-Experts

As one of the most complex and load-bearing joints in the human body, the ankle plays a crucial role in locomotion and clinical assessment. Accurate segmentation of ankle bones is essential for trauma evaluation, preoperative planning, and disease diagnosis. However, the scarcity of publicly available ankle datasets has hindered the development of intelligent analysis in this area. To address this gap, we introduce the first two publicly available ankle CT datasets, providing new directions for medical image segmentation. Considering the intricate and spatially correlated anatomy of the ankle, we propose a 3D medical image segmentation model, namely 3DABSeg. Specifically, we introduce the Hybrid KAN-Mamba Block (HKMB) to capture long-range spatial dependencies and complex nonlinearities. By integrating Mamba’s sequence modeling with KAN’s learnable spline functions, HKMB enhances feature expressiveness for complex anatomical structures. Furthermore, to address the uneven semantic distribution and feature dilution across channels, we propose a Multi-Scale Feature Fusion Mixture-of-Experts module (MFF-MoE). MFF-MoE utilizes multi-scale spatial pooling to compute dynamic routing scores, adaptively partitioning channels into specialized expert networks via a mutually exclusive hard-assignment strategy. Extensive experiments demonstrate that 3DABSeg surpasses existing state-of-the-art methods in both segmentation performance and boundary precision, highlighting its potential for complex anatomical structure analysis and clinical applications.