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.

提出了一种快速的粗到细的离散优化方法 SAMConvex，用于 CT 注册，包括根据捕捉局部和全局信息的自监督解剖嵌入器（SAM）特征提取器通过解耦凸优化过程获取变形场。SAMConvex 在两个患者间注册数据集（腹部 CT 和头颈 CT）和一个患者内注册数据集（肺部 CT）上优于现有的基于学习和优化的方法。此外，作为一种基于优化的方法，SAMConvex 仅需要大约 2 秒（使用实例优化约 5 秒）处理一对图像。