用于无监督医学图像配准的轻量级混合Mamba2

IF 3.2 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Medical physics Pub Date : 2025-09-10 DOI:10.1002/mp.18104
Aobo Xu, Shaofei Shen, Wenkang Chen, Xuejun Zhang
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引用次数: 0

摘要

变形医学图像配准是医学成像辅助诊断和治疗的关键任务。近年来,基于深度学习的医学图像配准方法利用先验知识取得了显著的成功,配准精度和计算效率得到了极大的提高。基于transformer的模型在图像配准方面取得了比卷积神经网络方法(ConvNet)更好的性能。然而,它们的二级计算复杂性导致了巨大的计算开销,为在资源受限的医疗环境中部署它们带来了巨大挑战。最近,Mamba-2引入了结构化状态空间对偶性(SSD)框架,以解决Transformer的高计算成本,实现跨多个领域的最先进性能。在图像配准领域,Mamba-2可能是比Transformer更强大的竞争对手。其全局接受野的设计和线性计算复杂度使其在准确理解运动图像和固定图像之间的非线性空间关系方面显示出巨大的优势和效率。为了解决资源受限的医疗环境下的部署挑战,进一步提高医学图像配准的效率和准确性,我们提出了用于医学图像配准的轻量级混合Mamba2模型HybridMorph。在本研究中,我们还介绍了三个不同参数数的HybridMorph版本。方法提出了残差混合模块(RHM),该模块基于卷积和Mamba-2重构了医学图像配准任务的特征提取模块,并提出了一种新的轻量级方法,称为并行通道特征聚合器(PCFA),该方法以较低的计算开销提取更丰富的特征表示。结果将该模型与现有的各种基线配准方法进行了比较。结果表明,与基线方法相比,HybridMorph取得了显著的性能改进,在atlas-对患者和患者间脑磁共振成像(MRI)配准中分别获得了0.780和0.824的最高平均Dice分数。值得注意的是,与著名的TransMorph相比,HybridMorph实现了卓越的配准性能,同时将参数数量和计算成本分别减少了10.1倍和5.8倍。结论与基线方法相比,HybridMorph带来了显著的性能改进和更低的计算开销,证明了我们的模型在促进医学图像配准模型轻量化设计方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Lightweight hybrid Mamba2 for unsupervised medical image registration

Lightweight hybrid Mamba2 for unsupervised medical image registration

Lightweight hybrid Mamba2 for unsupervised medical image registration

Background

Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration. However, their secondary computational complexity leads to significant computational overhead, posing substantial challenges for deployment in resource-constrained medical environments. Recently, Mamba-2 introduced the structured state-space Duality (SSD) framework to address the high computational cost of Transformer, achieving state-of-the-art performance across multiple domains. Mamba-2 may be a more powerful competitor than Transformer in the field of image registration. The design of its global receptive field and linear computational complexity enable it to show substantial advantages and efficiency in accurately understanding the nonlinear spatial relationships between the moving images and fixed images.

Purpose

To address the challenges of deployment in resource-constrained medical environments and further improve the efficiency and accuracy of medical image registration, we propose HybridMorph, a lightweight hybrid Mamba2 model for medical image registration. In this study, we also introduce three versions of HybridMorph with different parameter numbers.

Methods

We propose a Residual Hybrid Module (RHM) that reconstructs a feature extraction module for medical image registration tasks based on convolution and Mamba-2, along with a novel lightweight method called the parallel channel feature aggregator (PCFA), which extracts richer feature representations with lower computational overhead.

Results

The proposed model was evaluated by comparing it with various existing baseline registration methods. The results show that HybridMorph achieves significant performance improvements over the baseline methods, achieving the highest average Dice scores of 0.780 and 0.824 in atlas-to-patient and inter-patient brain Magnetic Resonance Imaging (MRI) registration, respectively. Notably, compared to the renowned TransMorph, HybridMorph achieves superior registration performance while reducing the number of parameters and computational cost by 10.1 and 5.8 times, respectively.

Conclusions

HybridMorph brings significant performance improvements and lower computational overhead compared to baseline methods, demonstrating the potential of our model in promoting the lightweight design of medical image registration models.

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来源期刊
Medical physics
Medical physics 医学-核医学
CiteScore
6.80
自引率
15.80%
发文量
660
审稿时长
1.7 months
期刊介绍: Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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