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<p>3D visualization of hepatic vessels plays a crucial role in the diagnosis and surgical planning of liver diseases, which requires the accurate segmentation of vessel structures. However, accurate segmentation of hepatic vessels remains challenging. One major challenge is inconsistent vessel enhancement, caused by variations in contrast agent administration. Another is the complex topology and abundance of fine branches in the hepatic vasculature. Numerous studies have been dedicated to this task. Yet, they often fail to capture intricate features, as they underutilize prior knowledge needed for high-fidelity visualization. In this study, we introduce the Vessel-Aware Module (VAM), comprising a Tubular Prior Fusion Block, a Neighboring Voxel Perception Block and a Projection Attention Block, to encode vessel topology, local context and spatial priors. This enhances the detection of fine vessels and generates more complete and visually coherent vascular maps. Integrating VAM into U-Mamba forms the VA-UMamba framework. Coupled with a novel Dilated Dice Loss, VA-UMamba achieves competitive performance on our high-quality dataset and two public datasets. In terms of Tree Length Detected (TD), VA-UMamba reached 74.70%, 78.72% and 78.64% on three datasets, which are 6.25%, 1.49% and 1.91% higher than the existing SOTA methods. It demonstrates the superior capability in recovering fine branches for accurate vascular visualization. The results also show significant improvements in other metrics, including Dice, Sensitivity and Hausdorff distance. Our method provides enhanced visual representations of hepatic vessels, supporting clinicians in preoperative planning through clearer anatomical insight. The code is available at: <a href="https://github.com/chen-x-h/VA-UMamba">https://github.com/chen-x-h/VA-UMamba</a>.</p>

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Multi-prior-guided deep learning framework for hepatic vessel segmentation

  • 期刊论文

  • 发表日期：

    2026年3月22日

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  • Qin Zhang,
  • Xianhao Chen,
  • Yingfang Fan,
  • Xiaodong Zhang

摘要

3D visualization of hepatic vessels plays a crucial role in the diagnosis and surgical planning of liver diseases, which requires the accurate segmentation of vessel structures. However, accurate segmentation of hepatic vessels remains challenging. One major challenge is inconsistent vessel enhancement, caused by variations in contrast agent administration. Another is the complex topology and abundance of fine branches in the hepatic vasculature. Numerous studies have been dedicated to this task. Yet, they often fail to capture intricate features, as they underutilize prior knowledge needed for high-fidelity visualization. In this study, we introduce the Vessel-Aware Module (VAM), comprising a Tubular Prior Fusion Block, a Neighboring Voxel Perception Block and a Projection Attention Block, to encode vessel topology, local context and spatial priors. This enhances the detection of fine vessels and generates more complete and visually coherent vascular maps. Integrating VAM into U-Mamba forms the VA-UMamba framework. Coupled with a novel Dilated Dice Loss, VA-UMamba achieves competitive performance on our high-quality dataset and two public datasets. In terms of Tree Length Detected (TD), VA-UMamba reached 74.70%, 78.72% and 78.64% on three datasets, which are 6.25%, 1.49% and 1.91% higher than the existing SOTA methods. It demonstrates the superior capability in recovering fine branches for accurate vascular visualization. The results also show significant improvements in other metrics, including Dice, Sensitivity and Hausdorff distance. Our method provides enhanced visual representations of hepatic vessels, supporting clinicians in preoperative planning through clearer anatomical insight. The code is available at: https://github.com/chen-x-h/VA-UMamba.