# MambaReg: Mamba-Based Disentangled Convolutional Sparse Coding for Unsupervised Deformable Multi-Modal Image Registration

arXiv: https://arxiv.org/abs/2411.01399 · HTML: https://mlsanity.com/paper/2411.01399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model](https://mlsanity.com/paper/1709.03966/) (2017)
- [Depth-aware CNN for RGB-D Segmentation](https://mlsanity.com/paper/1803.06791/) (2018)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations](https://mlsanity.com/paper/1903.09331/) (2019)
- [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019)
- [CoMIR: Contrastive Multimodal Image Representation for Registration](https://mlsanity.com/paper/2006.06325/) (2020)
- [LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation](https://mlsanity.com/paper/2106.04067/) (2021)
- [Learned Convolutional Sparse Coding](https://mlsanity.com/paper/1711.00328/) (2017)
- [Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration](https://mlsanity.com/paper/2007.02790/) (2020)
- [A Deep Metric for Multimodal Registration](https://mlsanity.com/paper/1609.05396/) (2016)

## Built on by (most cited first, 1 of 1)

- [Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies](https://mlsanity.com/paper/2512.01913/) (2025)

## Abstract (arXiv)

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.