Encoding Matching Criteria for Cross-domain Deformable Image Registration
<|reference_start|>Encoding Matching Criteria for Cross-domain Deformable Image Registration: 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.<|reference_end|>
@article{wang2024encoding,
    title={Encoding Matching Criteria for Cross-domain Deformable Image
  Registration},
    author={Zhuoyuan Wang, Haiqiao Wang and Yi Wang},
    journal={arXiv preprint arXiv:2406.12350},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12350},
    primaryClass={cs.CV}
}