  In this work, we introduce a fast and accurate method for unsupervised 3D
medical image registration. This work is built on top of a recent algorithm
SAM, which is capable of computing dense anatomical/semantic correspondences
between two images at the pixel level. Our method is named SAME, which breaks
down image registration into three steps: affine transformation, coarse
deformation, and deep deformable registration. Using SAM embeddings, we enhance
these steps by finding more coherent correspondences, and providing features
and a loss function with better semantic guidance. We collect a multi-phase
chest computed tomography dataset with 35 annotated organs for each patient and
conduct inter-subject registration for quantitative evaluation. Results show
that SAME outperforms widely-used traditional registration techniques (Elastix
FFD, ANTs SyN) and learning based VoxelMorph method by at least 4.7% and 2.7%
in Dice scores for two separate tasks of within-contrast-phase and
across-contrast-phase registration, respectively. SAME achieves the comparable
performance to the best traditional registration method, DEEDS (from our
evaluation), while being orders of magnitude faster (from 45 seconds to 1.2
seconds).
