Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization

TL;DR

This paper introduces an unsupervised multimodal image registration method using multilevel correlation pyramidal optimization, effectively addressing intraoperative deformation challenges and achieving top performance in the ReMIND2Reg task.

Contribution

The proposed MCPO method innovatively combines feature extraction and multilevel pyramidal optimization for robust multimodal registration, outperforming existing approaches.

Findings

Achieved first place in ReMIND2Reg validation and test phases.

Attained an average TRE of 1.798 mm on the Resect dataset.

Demonstrated broad applicability in preoperative-intraoperative registration.

Abstract

Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the target area to critical anatomical structures during surgery. However, due to the differences between multimodal images and intraoperative image deformation caused by tissue displacement and removal during the surgery, effective registration of preoperative and intraoperative multimodal images faces significant challenges. To address the multimodal image registration challenges in Learn2Reg 2025, an unsupervised multimodal medical image registration method based on Multilevel Correlation Pyramidal Optimization (MCPO) is designed to solve these problems. First, the features of each modality are extracted based on the modality independent neighborhood descriptor, and the multimodal images is mapped to the feature space. Second, a multilevel pyramidal fusion optimization mechanism is designed to achieve global optimization and local detail complementation of the displacement field through dense correlation analysis and weight-balanced coupled convex optimization for input features at different scales. Our method focuses on the ReMIND2Reg task in Learn2Reg 2025. Based on the results, our method achieved the first place in the validation phase and test phase of ReMIND2Reg. The MCPO is also validated on the Resect dataset, achieving an average TRE of 1.798 mm. This demonstrates the broad applicability of our method in preoperative-to-intraoperative image registration. The code is available at https://github.com/wjiazheng/MCPO.