Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead to varying tissue structures, or where image quality is poor, these methods tend to exhibit instability and inaccuracies. In this study, we propose a novel method for medical image registration based on arbitrary voxel point of interest matching, called query point quizzer (QUIZ). QUIZ focuses on the correspondence between local-global matching points, specifically employing CNN for feature extraction and utilizing the Transformer architecture for global point matching queries, followed by applying average displacement for local image rigid transformation. We have validated this approach on a large deformation dataset of cervical cancer patients, with results indicating substantially smaller deviations compared to state-of-the-art methods. Remarkably, even for cross-modality subjects, it achieves results surpassing the current state-of-the-art.

通过提出一种基于任意体素兴趣点匹配的医学图像配准新方法 QUIZ（query point quizzer），利用卷积神经网络进行特征提取和 Transformer 结构提取全局匹配点查询，通过平均位移进行局部图像刚体变换，我们验证了该方法在大变形数据集（宫颈癌患者）上的有效性，结果表明与当前最先进方法相比具有更小的偏差，令人瞩目的是，即使在交叉模态主题下，该方法也取得了超过当前最先进方法的结果。

QUIZ：医学影像配准的任意体积点匹配方法