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.

翻译：血管内动脉瘤修复术（EVAR）术后长期死亡率仍然较高，主要原因是支架移植物密封区密封失效导致的术后破裂。利用中心线测量进行结构化CT检查可改善检出率，但现有工作流程需人工编辑中心线且需操作专家参与。我们提出一种基于Transformer框架的自动化协议驱动密封区评估方法，该方法将三维中心线追踪与基于嵌入的几何预测相结合。通过评估两种前沿的图像-图模型，分别实现随访CT图像中主动脉-髂动脉中心线的提取，以及依据EVAR4C协议对支架位置、血管直径和密封长度的测量。在完整测试集和具有挑战性的无造影剂子集上，所提出的全自动方法性能优于商业半自动工作流程。