基于多级相关性平衡优化的无监督多模态3D医学图像配准
Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization
AI总结：
提出基于多级相关性平衡优化（MCBO）的无监督多模态3D医学图像配准方法，通过模态独立特征提取与多级金字塔融合优化变形场，在Learn2Reg 2024的ReMIND2Reg和COMULIS3DCLEM任务验证中均获第二名。
摘要：
基于多模态图像配准的手术导航通过在手术期间展示目标区域与关键解剖结构的相对位置，在为外科医生提供术中指导方面发挥了重要作用。然而，由于多模态图像之间的差异以及手术期间组织位移和切除引起的术中图像变形，术前与术中多模态图像的有效配准面临重大挑战。为应对Learn2Reg 2024中的多模态图像配准挑战，设计了一种基于多级相关性平衡优化（MCBO）的无监督多模态医学图像配准方法来解决这些问题。首先，基于模态独立邻域描述符提取每种模态的特征，并将多模态图像映射到特征空间。其次，设计了多级金字塔融合优化机制，通过对不同尺度的输入特征进行密集相关性分析和权重平衡的耦合凸优化，实现变形场的全局优化和局部细节补充。对于不同模态的术前医学图像，通过变形场之间的最大融合实现不同模态之间有效信息的对齐和堆叠。我们的方法聚焦于Learn2Reg 2024中的ReMIND2Reg任务，为验证方法的通用性，我们还在COMULIS3DCLEM任务上进行了测试。根据结果，我们的方法在两项任务的验证中均获得了第二名。代码可在https://github.com/wjiazheng/MCBO获取。
英文摘要：
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 surgery, effective registration of preoperative and intraoperative multimodal images faces significant challenges. To address the multimodal image registration challenges in Learn2Reg 2024, an unsupervised multimodal medical image registration method based on multilevel correlation balanced optimization (MCBO) 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 are mapped to the feature space. Second, a multilevel pyramidal fusion optimization mechanism is designed to achieve global optimization and local detail complementation of the deformation field through dense correlation analysis and weight-balanced coupled convex optimization for input features at different scales. For preoperative medical images in different modalities, the alignment and stacking of valid information between different modalities is achieved by the maximum fusion between deformation fields. Our method focuses on the ReMIND2Reg task in Learn2Reg 2024, and to verify the generality of the method, we also tested it on the COMULIS3DCLEM task. Based on the results, our method achieved second place in the validation of both two tasks. The code is available at https://github.com/wjiazheng/MCBO.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
