SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings

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 self-supervised anatomical embedding (SAM), which is capable of computing dense anatomical/semantic correspondences between two images at the pixel level. Our method is named SAM-enhanced registration (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 s to 1.2 s).