Seg2Reg: Structure-Preserving Appearance Randomization for Multimodal Retinal Image Registration

Multimodal retinal image registration (MRIR) aims to precisely align retinal vascular structures across modalities, supporting clinical diagnosis and treatment planning. However, the development of MRIR is hindered by the scarcity of large-scale annotated multimodal retinal image pairs. To address this issue, we propose a Seg2Reg learning framework that employs Retinal Structure-Preserving Appearance Randomization (RSPAR) to construct a synthetic registration dataset from retinal vessel segmentation data. RSPAR generates image pairs with randomized appearances while preserving vascular structure and exact geometric correspondences. The preserved vascular structures and exact geometric correspondences provide supervision for vessel-aware keypoint detection and feature matching, respectively, enabling registration learning without manually registered pairs. Beyond the limited training data, strong cross-modal appearance variations can cause anatomically corresponding locations to exhibit substantially different local features, leading to ambiguous feature matching. Motivated by this observation, we introduce Retinal Vascular Geometric Consistency (RVGC) to characterize whether candidate correspondences agree with the global retinal vascular geometry, and develop an RVGC Module for learned reliability-guided correspondence filtering and geometric estimation. Extensive experiments on the public CFFA benchmark and the private MICRO dataset demonstrate that Seg2Reg achieves state-of-the-art multimodal retinal image registration performance among the evaluated methods.