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<p>Medical image segmentation plays a pivotal role in clinical diagnosis and biomedical research. However, Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies, whereas Transformers—despite their global modeling capability—incur quadratic computational complexity. Recently, State-Space Models (SSMs), exemplified by Mamba, have enabled linear-complexity modeling of long-range dependencies. However, existing Mamba-based models still exhibit limitations in capturing local textures and edges and modeling spatial continuity. To address this challenge, we propose LEAP-Mamba (Locality-Enhanced Feature Calibration with Pixel–Region Dual-Stream Vision Mamba UNet). Specifically, we introduce a Pixel–Region VSSblock that comprises a local-Mamba branch and a global-Mamba branch to simultaneously capture fine-grained details and high-level semantic context. Furthermore, we design a Hierarchical Feature Calibration Module (HFCM) that leverages multi-scale context awareness and a structural saliency filtering mechanism to dynamically calibrate the skip connections, effectively suppressing feature redundancy while enhancing semantic interaction quality. Additionally, we propose a Cross-Region Feature Interaction (CRFI) module that aggregates spatial context via clustering to facilitate efficient inter-region interaction and semantic sharing. Extensive experiments on the ISIC2016, ISIC2017, ISIC2018, and Synapse datasets demonstrate that LEAP-Mamba achieves competitive performance compared with state-of-the-art methods, with leading results on several key metrics and notable improvements in challenging categories such as small organs and low-contrast structures. The code will be made available at <a href="https://github.com/caihanyue49-art/LEAP-Mamba">https://github.com/caihanyue49-art/LEAP-Mamba</a>.</p>

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Leap-mamba: locality-enhanced feature calibration with pixel-region dual-stream vision mamba UNet for medical image segmentation

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Bin Ge,
  • Caihan Yue,
  • Chenxing Xia,
  • Junshuai Zheng,
  • Yifei Zhang

摘要

Medical image segmentation plays a pivotal role in clinical diagnosis and biomedical research. However, Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies, whereas Transformers—despite their global modeling capability—incur quadratic computational complexity. Recently, State-Space Models (SSMs), exemplified by Mamba, have enabled linear-complexity modeling of long-range dependencies. However, existing Mamba-based models still exhibit limitations in capturing local textures and edges and modeling spatial continuity. To address this challenge, we propose LEAP-Mamba (Locality-Enhanced Feature Calibration with Pixel–Region Dual-Stream Vision Mamba UNet). Specifically, we introduce a Pixel–Region VSSblock that comprises a local-Mamba branch and a global-Mamba branch to simultaneously capture fine-grained details and high-level semantic context. Furthermore, we design a Hierarchical Feature Calibration Module (HFCM) that leverages multi-scale context awareness and a structural saliency filtering mechanism to dynamically calibrate the skip connections, effectively suppressing feature redundancy while enhancing semantic interaction quality. Additionally, we propose a Cross-Region Feature Interaction (CRFI) module that aggregates spatial context via clustering to facilitate efficient inter-region interaction and semantic sharing. Extensive experiments on the ISIC2016, ISIC2017, ISIC2018, and Synapse datasets demonstrate that LEAP-Mamba achieves competitive performance compared with state-of-the-art methods, with leading results on several key metrics and notable improvements in challenging categories such as small organs and low-contrast structures. The code will be made available at https://github.com/caihanyue49-art/LEAP-Mamba.