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.

翻译：现有的大多数基于深度学习的配准方法主要针对同类型图像进行训练，以解决同域任务。然而，跨域可变形配准仍然具有挑战性。我们认为，传统配准方法中量身定制的匹配准则是其能够适用于不同领域的主要原因之一。受此启发，我们设计了一种面向配准的编码器，用于建模图像特征和结构特征的匹配准则，这有助于提升配准精度和适应性。具体而言，我们提出了一种通用特征编码器（Encoder-G）来捕获全面的医学图像特征，同时设计了一种结构特征编码器（Encoder-S）将结构自相似性编码为全局表示。在来自三个不同领域的图像上进行的广泛实验证明了所提方法的有效性。此外，通过使用单次学习更新 Encoder-S，我们的方法能够有效适应不同领域。代码已在 https://github.com/JuliusWang-7/EncoderReg 公开。