arXiv 2401.139342025-06-25cs.CV

MambaMorph：一个基于Mamba的医学MR-CT可变形配准框架

MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration

Image Processing Center, Beihang University, Beijing, China（北京航空航天大学图像处理中心）；Robotics Institute, Beihang University, Beijing, China（北京航空航天大学机器人研究所）；Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China（华中科技大学同济医院）

Tao Guo, Yinuo Wang, Shihao Shu, Weimin Yuan, Diansheng Chen, Zhouping Tang, Cai Meng, Xiangzhi Bai

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AI总结：

本文提出基于Mamba的多模态可变形配准框架MambaMorph，通过Mamba配准模块和轻量特征提取器实现高效长程对应建模与高维特征学习，并构建脑部MR-CT配准数据集SR-Reg，在多个数据集上显著提升配准精度且保持合理计算成本。

摘要：

捕捉不同模态间的体素级空间对应关系对医学图像分析至关重要。然而，当前的配准方法在配准精度和临床适用性方面不够实用。本文引入了MambaMorph，一个新颖的多模态可变形配准框架。具体而言，MambaMorph利用基于Mamba的配准模块和一个精细但简单的特征提取器，分别进行高效的长程对应建模和高维特征学习。此外，我们开发了一个标注良好的脑部MR-CT配准数据集SR-Reg，以解决多模态配准中数据稀缺的问题。为验证MambaMorph的多模态配准能力，我们在SR-Reg数据集和一个公开的T1-T2数据集上进行了定量实验。两个数据集上的实验结果表明，MambaMorph在配准精度上显著优于当前最先进的基于学习的配准方法。进一步研究强调了基于Mamba的配准模块和轻量级特征提取器的有效性，它们在保持合理计算成本和速度的同时实现了显著的配准质量。我们相信MambaMorph在医学图像配准的实际应用中具有巨大潜力。MambaMorph的代码可在https://github.com/Guo-Stone/MambaMorph获取。

英文摘要：

Capturing voxel-wise spatial correspondence across distinct modalities is crucial for medical image analysis. However, current registration approaches are not practical enough in terms of registration accuracy and clinical applicability. In this paper, we introduce MambaMorph, a novel multi-modality deformable registration framework. Specifically, MambaMorph utilizes a Mamba-based registration module and a fine-grained, yet simple, feature extractor for efficient long-range correspondence modeling and high-dimensional feature learning, respectively. Additionally, we develop a well-annotated brain MR-CT registration dataset, SR-Reg, to address the scarcity of data in multi-modality registration. To validate MambaMorph's multi-modality registration capabilities, we conduct quantitative experiments on both our SR-Reg dataset and a public T1-T2 dataset. The experimental results on both datasets demonstrate that MambaMorph significantly outperforms the current state-of-the-art learning-based registration methods in terms of registration accuracy. Further study underscores the efficiency of the Mamba-based registration module and the lightweight feature extractor, which achieve notable registration quality while maintaining reasonable computational costs and speeds. We believe that MambaMorph holds significant potential for practical applications in medical image registration. The code for MambaMorph is available at: https://github.com/Guo-Stone/MambaMorph.

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