自动提取血管中心线，精准评估动脉瘤修复术后密封效果。
CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair
- 用Transformer模型自动追踪血管中心线并预测几何特征。
- 在无对比剂数据上仍优于商用半自动流程，准确率更高。
- 适合介入放射科医生快速评估术后修复质量，提升效率。
主动脉瘤腔内修复术（EVAR）后长期死亡率仍高，主要因支架密封区失去密封导致破裂。虽基于中心线测量的结构化CT阅片可提升检测能力，但现有流程需人工编辑中心线且依赖专家操作。本文提出一种基于Transformer的自动化、协议驱动的密封区评估框架，结合3D中心线追踪与嵌入式几何预测。评估了两种先进图像到图模型在随访CT中提取腹主动脉-髂动脉中心线的性能，并依据EVAR4C协议测量支架位置、血管直径及密封长度。在完整测试集和具有挑战性的无对比剂子集上，所提全自动方法均优于商用半自动工作流。
原文摘要 · Abstract (English)
Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft sealing zones. Structured CT review using centerline measurements improves detection, but current workflows require manual centerline editing and expert operators. We propose a transformer framework for automated, protocol-driven sealing zone assessment that combines 3D centerline tracking with embedding-based geometric prediction. Two state-of-the-art image-to-graph models are evaluated for aorto-iliac centerline extraction from follow-up CT and for measurement of stent position, vessel diameters, and seal lengths according to EVAR4C protocol. Across the full test set and a challenging no-contrast subset, the proposed fully automatic method outperforms the commercial semi-automatic workflow.
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