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2025-06-29 更新
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Authors:Tao Guo, Yinuo Wang, Shihao Shu, Weimin Yuan, Diansheng Chen, Zhouping Tang, Cai Meng, Xiangzhi Bai
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.
在医学图像分析中,捕获不同模态之间的体素级空间对应关系是至关重要的。然而,当前的注册方法并不够实用,在注册准确性和临床适用性方面有待提高。在本文中,我们介绍了MambaMorph,这是一种新型的多模态可变形注册框架。具体来说,MambaMorph利用基于Mamba的注册模块和精细且简单的特征提取器,分别进行高效的长程对应建模和高维特征学习。此外,我们开发了一个经过良好注释的脑部MR-CT注册数据集SR-Reg,以解决多模态注册中数据稀缺的问题。为了验证MambaMorph的多模态注册能力,我们在自己的SR-Reg数据集和公共T1-T2数据集上进行了定量实验。在两个数据集上的实验结果都表明,在注册准确性方面,MambaMorph显著优于当前最先进的基于学习的注册方法。进一步的研究强调了基于Mamba的注册模块和轻量级特征提取器的效率,它们在保持合理的计算成本和速度的同时,实现了显著的注册质量。我们相信MambaMorph在医学图像注册的实际应用中具有巨大的潜力。MambaMorph的代码可在https://github.com/Guo-Stone/MambaMorph获得。
论文及项目相关链接
Summary
MambaMorph是一种新型的多模态可变形注册框架,采用Mamba基础的注册模块和精细特征提取器,用于高效的长程对应建模和高维特征学习。它解决了多模态注册中数据缺乏的问题,并在SR-Reg数据集和公共T1-T2数据集上进行了实验验证,显著优于现有的基于学习的注册方法。
Key Takeaways
- MambaMorph是一种多模态可变形注册框架,旨在提高医学图像分析中的跨模态空间对应性。
- MambaMorph采用Mamba基础的注册模块,实现高效的长程对应建模。
- 框架使用精细但简单的特征提取器进行高维特征学习。
- 解决了多模态注册中数据缺乏的问题,通过开发SR-Reg数据集。
- 在SR-Reg数据集和公共T1-T2数据集上的实验验证了MambaMorph的注册能力。
- MambaMorph显著优于现有的基于学习的注册方法。
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