SAMConvex: Fast Discrete Optimization for CT Registration using
  Self-supervised Anatomical Embedding and Correlation Pyramid
Zi Li Lin Tian Tony C. W. Mok Xiaoyu Bai Puyang Wang Jia Ge Jingren Zhou Le Lu Xianghua Ye Ke Yan Dakai Jin
Estimating displacement vector field via a cost volume computed in the
feature space has shown great success in image registration, but it suffers
excessive computation burdens. Moreover, existing feature descriptors only
extract local features incapable of representing the global semantic
information, which is especially important for solving large transformations.
To address the discussed issues, we propose SAMConvex, a fast coarse-to-fine
discrete optimization method for CT registration that includes a decoupled
convex optimization procedure to obtain deformation fields based on a
self-supervised anatomical embedding (SAM) feature extractor that captures both
local and global information. To be specific, SAMConvex extracts per-voxel
features and builds 6D correlation volumes based on SAM features, and
iteratively updates a flow field by performing lookups on the correlation
volumes with a coarse-to-fine scheme. SAMConvex outperforms the
state-of-the-art learning-based methods and optimization-based methods over two
inter-patient registration datasets (Abdomen CT and HeadNeck CT) and one
intra-patient registration dataset (Lung CT). Moreover, as an
optimization-based method, SAMConvex only takes $\sim2$s ($\sim5s$ with
instance optimization) for one paired images.