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
https://github.com/alibaba-damo-academy/same.

我们介绍了一种快速而准确的无监督三维医学图像配准方法，利用自我监督的解剖嵌入（SAM）算法在像素级别计算两幅图像之间的密集解剖对应关系，并通过提供更为一致的对应关系和更好的语义引导特征来增强配准步骤，从而显著优于基于数值优化的方法。