Papers › Encoding Matching Criteria for Cross-domain Deformable Image Registration
Encoding Matching Criteria for Cross-domain Deformable Image Registration
Zhuoyuan Wang, Haiqiao Wang, Yi Wang
Most existing deep learning-based registration methods are trained on single-type images to address same-domain tasks.However, cross-domain deformable registration remains challenging.We argue that the tailor-made matching criteria in traditional registration methods is one of the main reason they are applicable in different domains.Motivated by this, we devise a registration-oriented encoder to model the matching criteria of image features and structural features, which is beneficial to boost registration accuracy and adaptability.Specifically, a general feature encoder (Encoder-G) is proposed to capture comprehensive medical image features, while a structural feature encoder (Encoder-S) is designed to encode the structural self-similarity into the global representation.Extensive experiments on images from three different domains prove the efficacy of the proposed method. Moreover, by updating Encoder-S using one-shot learning, our method can effectively adapt to different domains.The code is publicly available at https://github.com/JuliusWang-7/EncoderReg.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections