  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.
