mlsanityData to 14 Oct 2026

HybridMamba: A Dual-domain Mamba for 3D Medical Image Segmentation

arXiv 2509.14609 ↗First posted 18 Sep 2025cs.CVcited by 0

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

In the domain of 3D biomedical image segmentation, Mamba exhibits the superior performance for it addresses the limitations in modeling long-range dependencies inherent to CNNs and mitigates the abundant computational overhead associated with Transformer-based frameworks when processing high-resolution medical volumes. However, attaching undue importance to global context modeling may inadvertently compromise critical local structural information, thus leading to boundary ambiguity and regional distortion in segmentation outputs. Therefore, we propose the HybridMamba, an architecture employing dual complementary mechanisms: 1) a feature scanning strategy that progressively integrates representations both axial-traversal and local-adaptive pathways to harmonize the relationship between local and global representations, and 2) a gated module combining spatial-frequency analysis for comprehensive contextual modeling. Besides, we collect a multi-center CT dataset related to lung cancer. Experiments on MRI and CT datasets demonstrate that HybridMamba significantly outperforms the state-of-the-art methods in 3D medical image segmentation.

Key facts. No citing papers in the map yet (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 22 of its 30 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

22 of 30 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model6 Sep
  2. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining5 Feb
  3. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation24 Jan
  4. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  6. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  7. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  8. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 605
  9. 2019Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networkscited by 368
  10. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
  11. 2024LocalMamba: Visual State Space Model with Windowed Selective Scancited by 236
  12. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 162
6 more
  1. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  2. 2024RainMamba: Enhanced Locality Learning with State Space Models for Video Derainingcited by 56
  3. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentationcited by 41
  4. 2023Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitalscited by 25
  5. 2023HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentationcited by 12
  6. 2024Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularizationcited by 11

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation
  2. 2026A Hybrid Mamba-SAM Architecture for Efficient 3D Medical Image Segmentation
  3. 2025MambaCAFU: Hybrid Multi-Scale and Multi-Attention Model with Mamba-Based Fusion for Medical Image Segmentation
  4. 2025DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
  5. 2025MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation
  6. 2025TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation
  7. 2024MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation
  8. 2024HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation