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<p>State Space Models (SSMs), particularly the Mamba architecture, demonstrate significant potential in medical image segmentation due to the capabilities in modeling long-range dependencies. In the realm of medical image analysis, Vision Mamba UNet (VM-UNet) employs an asymmetric encoder-decoder architecture featuring Visual State Space blocks to capture extended spatial contextual information. However, VM-UNet encounters critical limitations: 1) Its restricted multi-scale modeling capacity during feature extraction impedes the effective fusion of local and global features, ultimately diminishing segmentation accuracy and robustness; 2) When processing complex lesion morphologies, certain boundary information loss occurs. This affects the method’s applicability in precision-sensitive medical imaging scenarios. To address these limitations, we propose MVM-UNet, a multi-branch convolutional VM-UNet architecture. By extending VM-UNet, our model integrates a novel convolutional state-space module comprising parallel branches dedicated to capturing local and global features across varying receptive fields. The subsequent feature fusion operations enhance both representational capacity and generalization potential. Specifically, MVM-UNet achieves computational efficiency through depth-wise separable convolutional branches while utilizing heterogeneous depth-wise convolutions with multi-scale kernels for optimal texture characterization in complex lesion regions. Furthermore, the architecture incorporates a hybrid normalization scheme combining BatchNorm and LayerNorm with parametric ReLU activation, significantly improving training stability and feature discriminability. Comprehensive evaluations on the ISIC17 and ISIC18 skin lesion benchmarks, supplemented by a proprietary kidney tumor dataset, demonstrate the superiority of MVM-UNet over existing approaches. Quantitative analyses reveal significant improvements in DSC (increased by 0.37%) and mIoU (increased by 0.71%), statistically validating the framework’s effectiveness in medical image segmentation tasks.</p>

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MVM-UNet: multi branch convolutional vision Mamba UNet for medical image segmentation

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Shaojia Niu,
  • Huimin Pan,
  • Quanli Gao,
  • Lianhe Shao

摘要

State Space Models (SSMs), particularly the Mamba architecture, demonstrate significant potential in medical image segmentation due to the capabilities in modeling long-range dependencies. In the realm of medical image analysis, Vision Mamba UNet (VM-UNet) employs an asymmetric encoder-decoder architecture featuring Visual State Space blocks to capture extended spatial contextual information. However, VM-UNet encounters critical limitations: 1) Its restricted multi-scale modeling capacity during feature extraction impedes the effective fusion of local and global features, ultimately diminishing segmentation accuracy and robustness; 2) When processing complex lesion morphologies, certain boundary information loss occurs. This affects the method’s applicability in precision-sensitive medical imaging scenarios. To address these limitations, we propose MVM-UNet, a multi-branch convolutional VM-UNet architecture. By extending VM-UNet, our model integrates a novel convolutional state-space module comprising parallel branches dedicated to capturing local and global features across varying receptive fields. The subsequent feature fusion operations enhance both representational capacity and generalization potential. Specifically, MVM-UNet achieves computational efficiency through depth-wise separable convolutional branches while utilizing heterogeneous depth-wise convolutions with multi-scale kernels for optimal texture characterization in complex lesion regions. Furthermore, the architecture incorporates a hybrid normalization scheme combining BatchNorm and LayerNorm with parametric ReLU activation, significantly improving training stability and feature discriminability. Comprehensive evaluations on the ISIC17 and ISIC18 skin lesion benchmarks, supplemented by a proprietary kidney tumor dataset, demonstrate the superiority of MVM-UNet over existing approaches. Quantitative analyses reveal significant improvements in DSC (increased by 0.37%) and mIoU (increased by 0.71%), statistically validating the framework’s effectiveness in medical image segmentation tasks.