QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image
  Registration
Lin Liu Xinxin Fan Haoyang Liu Chulong Zhang Weibin Kong Jingjing Dai Yuming Jiang Yaoqin Xie Xiaokun Liang
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.