QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
This addresses instability and inaccuracies in medical image registration for scenarios like cervical cancer, offering incremental improvements over existing methods.
The paper tackles the problem of medical image registration under large deformations and poor image quality by proposing QUIZ, a method based on arbitrary voxel point matching, which achieves substantially smaller deviations and surpasses state-of-the-art results even in cross-modality cases.
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.