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.

翻译：图像配准的目标是建立两幅或多幅图像之间的空间对应关系，传统方法采用密集位移场（DDF）或参数化变换（如刚性、仿射和样条）。通过重新思考通过空间变换实现对齐的现有范式，我们发现了一种替代但更直观的对应表示方式：一组感兴趣区域（ROI）对。我们证明，该表示方式具有与其他对应表示方法相当的表示能力。此外，这些ROI并不需要具备特定的解剖或语义意义，既非必要也非充分条件。进而，我们将图像配准形式化为从移动图像和固定图像中搜索同一组对应ROI——换言之，即在一对图像上执行两个多类分割任务。为实现通用且实用的实施，我们提出的算法集成了分割一切模型（SAM），从而形成一种无需任何训练数据、基于梯度的微调或工程化提示的SAM赋能配准（SAMReg）。我们通过实验证明，在配准前列腺MR、心脏MR和腹部CT图像的三个临床应用中，所提出的SAMReg能够分割并匹配多个ROI对，建立足够精确的对应关系。基于解剖结构上的Dice系数和目标配准误差等指标，我们提出的配准方法优于基于强度的迭代算法和预测DDF的学习型网络，甚至与需要完全分割训练数据的弱监督配准相比也展现出具有竞争力的性能。