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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 optimizesimilarity measures computed directly on intensities or on hand-craftedfeatures, which lack anatomical semantic information. These similarity measuresmay lead to sub-optimal solutions where large deformations, complex anatomicaldifferences, or cross-modality imagery exist. In this work, we introduce a fastand accurate method for unsupervised 3D medical image registration building ontop of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capableof computing dense anatomical correspondences between two images at the voxellevel. We name our approach SAM-Enhanced registration (SAME++), whichdecomposes image registration into four steps: affine transformation, coarsedeformation, deep non-parametric transformation, and instance optimization.Using SAM embeddings, we enhance these steps by finding more coherentcorrespondence and providing features with better semantic guidance. Weextensively evaluated SAME++ using more than 50 labeled organs on threechallenging inter-subject registration tasks of different body parts. As acomplete registration framework, SAME++ markedly outperforms leading methods by$4.2\%$ - $8.2\%$ in terms of Dice score while being orders of magnitude fasterthan numerical optimization-based methods. Code is available at\url{https://github.com/alibaba-damo-academy/same}.

SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation