基于混合专家（MoE）的特征适配器，用于X射线血管造影中无需提示的冠状动脉二值分割

中文摘要 AI 辅助

在X射线血管造影视频中准确分割冠状动脉，对定量冠脉分析和图像引导干预至关重要。然而，由于冠脉血管较细、对比度低，且导管、导丝及复杂解剖背景结构的存在会进一步干扰血管轮廓的勾勒，准确分割仍具挑战性。现有的基于U-Net和Transformer的模型提供了强劲的基线性能，但它们共享的特征适配通路可能不足以应对血管造影图像的异质性外观。本文提出一种无需提示的混合专家（MoE）特征适配器，用于冠状动脉二值分割。该方法构建于参数高效的视觉Transformer适配器之上，采用多个轻量级专家，通过依赖输入的Top-K路由机制自适应优化与血管相关的特征，同时限制主动计算成本。在MOSXAV数据集上的实验，以及在XACV数据集上的外部评估显示，所提方法优于代表性基线，且提升了跨数据集泛化能力。这些结果表明，基于MoE的适配器学习对X射线血管造影视频中鲁棒的冠状动脉分割是有效的。

英文摘要

Accurate segmentation of coronary arteries in X-ray angiography videos is essential for quantitative coronary analysis and image-guided interventions. However, accurate segmentation remains challenging because coronary vessels are thin and exhibit low contrast, while the presence of catheters, guidewires, and complex anatomical background structures can further interfere with vessel delineation. Existing U-Net- and Transformer-based models provide strong baselines, but their shared feature-adaptation pathways may be insufficient for heterogeneous angiographic appearances. In this paper, we propose a prompt-free mixture-of-experts (MoE) feature adapter for binary coronary artery segmentation. Built upon parameter-efficient Vision Transformer adapters, the proposed method uses multiple lightweight experts with input-dependent top-$k$ routing to adaptively refine vessel-related features while limiting active computational cost. Experiments on MOSXAV and external evaluation on XACV show that the proposed method outperforms representative baselines and improves cross-dataset generalisation. These results suggest that MoE-based adapter learning is effective for robust coronary artery segmentation in X-ray angiography videos.

发表机构

• University College London（伦敦大学学院）
• University of East Anglia（东英吉利大学）
• King’s College London（伦敦国王学院）

机构由 AI 辅助整理，请以论文原文为准。