MambaReg：用于无监督可变形多模态图像配准的基于Mamba的解耦卷积稀疏编码
MambaReg: Mamba-Based Disentangled Convolutional Sparse Coding for Unsupervised Deformable Multi-Modal Image Registration
AI总结：
针对多模态可变形图像配准中长程依赖难捕捉、特征难解耦的问题，提出基于Mamba的MambaReg架构，融合卷积局部特征提取与Mamba长程建模能力，解耦配准相关特征，在公开数据集上配准精度与形变场平滑度均优于现有方法。
摘要：
对存在固有特征差异的多模态图像进行精确对齐，是可变形图像配准领域的一项核心挑战。传统的基于学习的方法通常将配准网络视为缺乏可解释性的黑箱。一个核心洞见是，跨模态解耦对齐特征与非对齐特征能够带来增益。与此同时，卷积神经网络等图像配准任务的主流方法受限于局部感受野，难以捕捉长程依赖关系。当给定图像对存在较大错位时，这类方法由于无法有效学习长程依赖与对应关系，往往会失效。本文提出了一种全新的基于Mamba的架构MambaReg，它整合了Mamba在长序列捕捉方面的强大能力，以应对上述挑战。借助我们提出的若干子模块，MambaReg能够有效地将用于配准的模态无关特征，与依赖模态的非对齐特征解耦。通过选择性地关注相关特征，我们的网络能够精准捕捉多模态图像之间的关联，实现聚焦的形变场预测与精确的图像对齐。这种基于Mamba的架构将卷积层的局部特征提取能力与Mamba的长程依赖建模能力无缝融合。在公开的非刚性RGB-IR图像数据集上的实验证明了我们方法的优越性，其在配准精度和形变场平滑度方面均优于现有方法。
英文摘要：
Precise alignment of multi-modal images with inherent feature discrepancies poses a pivotal challenge in deformable image registration. Traditional learning-based approaches often consider registration networks as black boxes without interpretability. One core insight is that disentangling alignment features and non-alignment features across modalities bring benefits. Meanwhile, it is challenging for the prominent methods for image registration tasks, such as convolutional neural networks, to capture long-range dependencies by their local receptive fields. The methods often fail when the given image pair has a large misalignment due to the lack of effectively learning long-range dependencies and correspondence. In this paper, we propose MambaReg, a novel Mamba-based architecture that integrates Mamba's strong capability in capturing long sequences to address these challenges. With our proposed several sub-modules, MambaReg can effectively disentangle modality-independent features responsible for registration from modality-dependent, non-aligning features. By selectively attending to the relevant features, our network adeptly captures the correlation between multi-modal images, enabling focused deformation field prediction and precise image alignment. The Mamba-based architecture seamlessly integrates the local feature extraction power of convolutional layers with the long-range dependency modeling capabilities of Mamba. Experiments on public non-rigid RGB-IR image datasets demonstrate the superiority of our method, outperforming existing approaches in terms of registration accuracy and deformation field smoothness.
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。
