MedSAM-guided geometry-aware 2D-3D feature fusion for medical image registration

Medical image registration is essential for aligning heterogeneous imaging data, yet simultaneously capturing fine-grained local details while maintaining global spatial coherence remains a significant challenge. To address this challenge, we propose MGGA, an anatomy-guided, annotation-free framework that integrates the generalization ability of the MedSAM foundation model with a geometry-aware fusion strategy. Specifically, we leverage a fine-tuned MedSAM encoder to extract 2D slice-level structural priors, and associate them with anatomical semantics using a CLIP-based text encoder, thereby reducing sensitivity to intensity variations without requiring task-specific annotations. To bridge the dimensionality gap between these 2D representations and the 3D global context extracted by a parallel spatial encoder, we introduce a Geometry-Aware 2D-3D Feature Fusion Module (GA-FFM). This module utilizes implicit neural representations to project 2D features into volumetric space, guided by a dual-metric mechanism based on Gaussian and Cosine similarities to adaptively weight features according to geometric consistency. Furthermore, a dual-layer regularization strategy is employed to reinforce anatomical plausibility. Comprehensive evaluations on diverse benchmarks, covering both unimodal and multimodal tasks across brain MRI and abdominal CT datasets, demonstrate that MGGA consistently outperforms state-of-the-art methods. The observed robustness to pathological anatomy and anisotropic spacing, together with improved topological validity, suggests that the framework may be applicable to challenging clinical scenarios. The code is available at https://github.com/goghfan/MGGA.