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

arXiv: https://arxiv.org/abs/2401.13934 · HTML: https://mlsanity.com/paper/2401.13934/

## Key facts

- Cited by 10 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 15 of its 25 references that are in the map.
- First posted on arXiv 25 Jan 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, 15 of 15)

- [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)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [HyperMorph: Amortized Hyperparameter Learning for Image Registration](https://mlsanity.com/paper/2101.01035/) (2021)
- [CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration](https://mlsanity.com/paper/2008.05772/) (2020)
- [SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy](https://mlsanity.com/paper/2303.16320/) (2023)
- [XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention](https://mlsanity.com/paper/2206.07349/) (2022)
- [U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?](https://mlsanity.com/paper/2208.04939/) (2022)
- [Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training](https://mlsanity.com/paper/2303.00874/) (2023)
- [SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings](https://mlsanity.com/paper/2109.11572/) (2021)
- [ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration](https://mlsanity.com/paper/2206.13434/) (2022)
- [GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration](https://mlsanity.com/paper/2306.14687/) (2023)

## Built on by (most cited first, 10 of 10)

- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [Visual Mamba: A Survey and New Outlooks](https://mlsanity.com/paper/2404.18861/) (2024)
- [SpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding](https://mlsanity.com/paper/2509.19873/) (2025)
- [ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction](https://mlsanity.com/paper/2404.13257/) (2024)
- [MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction](https://mlsanity.com/paper/2505.09965/) (2025)
- [MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI](https://mlsanity.com/paper/2507.17678/) (2025)
- [Is Mamba Reliable for Medical Imaging?](https://mlsanity.com/paper/2602.16723/) (2026)
- [Neural Phase Correlation](https://mlsanity.com/paper/2606.18496/) (2026)
- [Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes](https://mlsanity.com/paper/2609.11240/) (2026)
- [Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy](https://mlsanity.com/paper/2610.18926/) (2026)

## Abstract (arXiv)

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.