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Background <p>Accurate vessel segmentation in X-ray angiography (XRA) is essential for diagnosing coronary artery disease and supporting interventional procedures. However, low contrast, complex backgrounds, and substantial variation in vessel caliber make accurate segmentation challenging, especially for thin vessels.</p> Methods <p>We propose a Vascular-Aware Mixture-of-Experts (VA-MoE) framework for XRA vessel segmentation. The model incorporates Dynamic Snake Convolution (DSnC) to capture the elongated and curved morphology of vessels and a Texture-Enhanced Decoder (TED) to recover fine spatial details and suppress background noise during upsampling. The proposed framework was evaluated on three representative datasets: ARCADE, DCA1, and XCAD.</p> Results <p>VA-MoE achieved an IoU of 87.80% and a Dice coefficient of 93.08% on ARCADE, an IoU of 65.18% and a Dice coefficient of 78.03% on DCA1, and an IoU of 67.52% and a Dice coefficient of 79.93% on XCAD. Compared with the strongest baseline methods, VA-MoE improved IoU and Dice by 2.77 and 1.21 percentage points on ARCADE, by 2.40 and 2.10 percentage points on DCA1, and by 2.89 and 1.50 percentage points on XCAD, respectively. Qualitative results further suggested improved topological continuity and better preservation of thin peripheral vessels compared with competing methods.</p> Conclusion <p>The proposed VA-MoE framework provides a promising solution for XRA vessel segmentation. By jointly enhancing vascular structural modeling and texture recovery, it may support automated cardiovascular image analysis.</p>

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Vascular-aware mixture-of-experts with a texture-enhanced decoder for accurate X-ray vessel segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Zhongjian Ju,
  • Qingnan Wu,
  • Wanqiang Cai,
  • Zidong Liu,
  • Ruigang Ge,
  • Yimeng Li,
  • Jinyuan Wang,
  • Zaichao Lu,
  • Zeping Cui,
  • Jiasong Wu,
  • Baolin Qu

摘要

Background

Accurate vessel segmentation in X-ray angiography (XRA) is essential for diagnosing coronary artery disease and supporting interventional procedures. However, low contrast, complex backgrounds, and substantial variation in vessel caliber make accurate segmentation challenging, especially for thin vessels.

Methods

We propose a Vascular-Aware Mixture-of-Experts (VA-MoE) framework for XRA vessel segmentation. The model incorporates Dynamic Snake Convolution (DSnC) to capture the elongated and curved morphology of vessels and a Texture-Enhanced Decoder (TED) to recover fine spatial details and suppress background noise during upsampling. The proposed framework was evaluated on three representative datasets: ARCADE, DCA1, and XCAD.

Results

VA-MoE achieved an IoU of 87.80% and a Dice coefficient of 93.08% on ARCADE, an IoU of 65.18% and a Dice coefficient of 78.03% on DCA1, and an IoU of 67.52% and a Dice coefficient of 79.93% on XCAD. Compared with the strongest baseline methods, VA-MoE improved IoU and Dice by 2.77 and 1.21 percentage points on ARCADE, by 2.40 and 2.10 percentage points on DCA1, and by 2.89 and 1.50 percentage points on XCAD, respectively. Qualitative results further suggested improved topological continuity and better preservation of thin peripheral vessels compared with competing methods.

Conclusion

The proposed VA-MoE framework provides a promising solution for XRA vessel segmentation. By jointly enhancing vascular structural modeling and texture recovery, it may support automated cardiovascular image analysis.