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

TL;DR

SAME is a fast, self-supervised 3D medical image registration method that leverages anatomical embeddings for improved accuracy and semantic coherence, outperforming traditional techniques in speed and accuracy.

Contribution

This paper introduces SAME, a novel registration approach that integrates SAM embeddings for enhanced semantic correspondence and efficiency in medical image alignment.

Findings

SAME outperforms traditional registration methods in Dice scores.

SAME is significantly faster, reducing registration time from 45s to 1.2s.

Achieves comparable accuracy to the best traditional method, DEEDS.

Abstract

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).