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<p>Accurate retinal vessel segmentation is crucial for diagnosing ocular and systemic diseases. Current methods struggle to balance noise discrimination with detail preservation, especially for capillary detection. This paper introduces TA-Mamba, a novel framework combining the State Space Model (SSM) Mamba with tubular structure perception modules. TA-Mamba utilizes a high and low frequency attention Mamba block, tubular-aware gated convolution, serpentine spatial convolution, directional feature fusion, and a convolution-up block. Experimental results on DRIVE, CHASE_DB1, and STARE datasets demonstrate TA-Mamba’s superior performance, outperforming state-of-the-art methods in accuracy, continuity, and topological connectivity. This work represents a significant advancement in retinal vessel segmentation, effectively addressing challenges posed by low contrast and high noise in retinal images.</p>

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Tubular-aware mamba for accurate retinal vessel segmentation: preserving fine details and topological connectivity

  • 期刊论文

  • 发表日期：

    2025年4月5日

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  • Dangguo Shao,
  • Rui Xu,
  • Lei Ma,
  • Sanli Yi

摘要

Accurate retinal vessel segmentation is crucial for diagnosing ocular and systemic diseases. Current methods struggle to balance noise discrimination with detail preservation, especially for capillary detection. This paper introduces TA-Mamba, a novel framework combining the State Space Model (SSM) Mamba with tubular structure perception modules. TA-Mamba utilizes a high and low frequency attention Mamba block, tubular-aware gated convolution, serpentine spatial convolution, directional feature fusion, and a convolution-up block. Experimental results on DRIVE, CHASE_DB1, and STARE datasets demonstrate TA-Mamba’s superior performance, outperforming state-of-the-art methods in accuracy, continuity, and topological connectivity. This work represents a significant advancement in retinal vessel segmentation, effectively addressing challenges posed by low contrast and high noise in retinal images.