Learning dual-scale context with overlap awareness for keypoint-driven partial-overlap medical image registration

Aligning medical images with partial anatomical overlap presents a significant challenge for various clinical applications that involve comparison of images with varying Fields of View. However, most existing registration methods implicitly assume full anatomical overlap and rely on local similarity measures, which limits their effectiveness when non-overlapping conditions exist. In this paper, we present a keypoint-driven registration framework for partial-overlap medical images, which builds robust cross-image correspondences by embedding anatomical contextual information directly into keypoint descriptors. The framework first detects sparse but anatomically representative keypoints and encodes each with a learned descriptor. These descriptors are then iteratively enhanced through a Dual-scale Context Aggregation Module (DualCAM), which jointly models global structures and local details to enhance descriptor discriminability. This process produces descriptors enriched with anatomical context for more reliable correspondence matching. Furthermore, we introduce an overlap-aware guidance mechanism that encourages the model to focus on the most reliable and anatomically consistent overlapping regions, mitigating interference from irrelevant non-overlapping regions and improving overall alignment accuracy. Extensive experiments conducted on two public multi-organ abdominal CT datasets demonstrate that our method surpasses state-of-the-art approaches across a wide range of overlap ratios, highlighting its robustness and strong generalization capability.