The goal of image registration is to establish spatial correspondence between
two or more images, traditionally through dense displacement fields (DDFs) or
parametric transformations (e.g., rigid, affine, and splines). Rethinking the
existing paradigms of achieving alignment via spatial transformations, we
uncover an alternative but more intuitive correspondence representation: a set
of corresponding regions-of-interest (ROI) pairs, which we demonstrate to have
sufficient representational capability as other correspondence representation
methods.Further, it is neither necessary nor sufficient for these ROIs to hold
specific anatomical or semantic significance. In turn, we formulate image
registration as searching for the same set of corresponding ROIs from both
moving and fixed images - in other words, two multi-class segmentation tasks on
a pair of images. For a general-purpose and practical implementation, we
integrate the segment anything model (SAM) into our proposed algorithms,
resulting in a SAM-enabled registration (SAMReg) that does not require any
training data, gradient-based fine-tuning or engineered prompts. We
experimentally show that the proposed SAMReg is capable of segmenting and
matching multiple ROI pairs, which establish sufficiently accurate
correspondences, in three clinical applications of registering prostate MR,
cardiac MR and abdominal CT images. Based on metrics including Dice and target
registration errors on anatomical structures, the proposed registration
outperforms both intensity-based iterative algorithms and DDF-predicting
learning-based networks, even yielding competitive performance with
weakly-supervised registration which requires fully-segmented training data.

图像配准的目标是通过稠密位移场或参数化变换（如刚性、仿射和样条）在两个或多个图像之间建立空间对应关系。本文提出了一种新的对应关系表示方法，即一组相应感兴趣区域（ROI）对，通过实验证明具有足够的表示能力。并将图像配准定义为在图像对上搜索相同对应 ROI 集合的问题，并且提出了一种不需要训练数据、梯度微调或工程化提示的 SAMReg 算法。实验证明，SAMReg 在三个临床应用中（前列腺 MR、心脏 MR 和腹部 CT 图像的配准）能够准确完成分割和匹配多个 ROI 对，且在解剖结构的 Dice 系数和目标配准误差等度量指标上表现优于基于强度的迭代算法和基于学习的预测位移场算法，甚至能够与需要完全分割的弱监督注册方法相媲美。