Learning Reliable Evidence for Coarse-to-Fine Deformable Medical Image Registration

Medical image registration is fundamental to quantitative anatomical analysis and the assessment of pathological changes. Existing methods commonly use multiscale features to progressively align global structures and local details. However, downsampling can introduce aliasing into coarse-scale representations when high-frequency components are insufficiently attenuated. The resulting changes in local feature responses can disrupt anatomical correspondence estimation. We propose EviGuardMorph, a framework that improves registration reliability through the construction and use of correspondence evidence. The Frequency-Biased Evidence Encoder (FBE) applies scale-dependent Gaussian prefiltering before downsampling to construct structure and detail evidence streams with different effective bandwidths. The detail stream is retained only at high-resolution scales, where it interacts with the structure stream through reversible coupling. The Baseline-Protected Bidirectional Matcher (BPBM) preserves the one-way local matching estimate as a baseline. It combines matching entropy with forward-backward cycle consistency to weight bidirectional candidate residuals, suppressing erroneous updates from ambiguous matches. Experiments demonstrate that EviGuardMorph achieves leading registration accuracy across multiple datasets. Spectral analysis further shows that prefiltering reduces the out-of-band spectral energy fraction before downsampling by 90.4%–99.7%.