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Estimating displacement vector field via a cost volume computed in thefeature space has shown great success in image registration, but it suffersexcessive computation burdens. Moreover, existing feature descriptors onlyextract local features incapable of representing the global semanticinformation, which is especially important for solving large transformations.To address the discussed issues, we propose SAMConvex, a fast coarse-to-finediscrete optimization method for CT registration that includes a decoupledconvex optimization procedure to obtain deformation fields based on aself-supervised anatomical embedding (SAM) feature extractor that captures bothlocal and global information. To be specific, SAMConvex extracts per-voxelfeatures and builds 6D correlation volumes based on SAM features, anditeratively updates a flow field by performing lookups on the correlationvolumes with a coarse-to-fine scheme. SAMConvex outperforms thestate-of-the-art learning-based methods and optimization-based methods over twointer-patient registration datasets (Abdomen CT and HeadNeck CT) and oneintra-patient registration dataset (Lung CT). Moreover, as anoptimization-based method, SAMConvex only takes $\sim2$s ($\sim5s$ withinstance optimization) for one paired images.

SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid