Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locations. However, existing methods often optimize similarity measures computed directly on intensities or on hand-crafted features, which lack anatomical semantic information. These similarity measures may lead to sub-optimal solutions where large deformations, complex anatomical differences, or cross-modality imagery exist. In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capable of computing dense anatomical correspondences between two images at the voxel level. We name our approach SAM-Enhanced registration (SAME++), which decomposes image registration into four steps: affine transformation, coarse deformation, deep non-parametric transformation, and instance optimization. Using SAM embeddings, we enhance these steps by finding more coherent correspondence and providing features with better semantic guidance. We extensively evaluated SAME++ using more than 50 labeled organs on three challenging inter-subject registration tasks of different body parts. As a complete registration framework, SAME++ markedly outperforms leading methods by $4.2\%$ - $8.2\%$ in terms of Dice score while being orders of magnitude faster than numerical optimization-based methods. Code is available at \url{https://github.com/alibaba-damo-academy/same}.

翻译：图像配准是一项基础的医学图像分析任务。理想情况下，配准应聚焦于对齐语义对应的体素，即相同的解剖位置。然而，现有方法常优化直接基于强度或手工特征计算的相似性度量，这些度量缺乏解剖语义信息。在大变形、复杂解剖差异或跨模态图像存在时，此类相似性度量可能导致次优解。本文提出一种基于自监督解剖嵌入算法的快速精确无监督三维医学图像配准方法，该算法能够计算两幅图像间体素级别的稠密解剖对应关系。我们将该方法命名为SAM增强配准（SAME++），将图像配准分解为四个步骤：仿射变换、粗变形、深度非参数变换和实例优化。通过利用SAM嵌入，我们能够找到更一致的对应关系并提供具有更好语义引导的特征，从而增强上述步骤。我们使用涵盖三个不同身体部位的具有挑战性的跨个体配准任务中的50余个标注器官，对SAME++进行了全面评估。作为完整的配准框架，SAME++在Dice分数上以4.2%-8.2%的优势显著领先主流方法，同时比基于数值优化的方法快数个数量级。代码已开源至\url{https://github.com/alibaba-damo-academy/same}。