Coronary angiography remains the gold standard for diagnosis of coronary artery disease, the most common cause of death worldwide. While this procedure is performed more than 2 million times annually, there remain few methods for fast and accurate automated measurement of disease and localization of coronary anatomy. Here, we present our solution to the Automatic Region-based Coronary Artery Disease diagnostics using X-ray angiography images (ARCADE) challenge held at MICCAI 2023. For the artery segmentation task, our three-stage approach combines preprocessing and feature selection by classical computer vision to enhance vessel contrast, followed by an ensemble model based on YOLOv8 to propose possible vessel candidates by generating a vessel map. A final segmentation is based on a logic-based approach to reconstruct the coronary tree in a graph-based sorting method. Our entry to the ARCADE challenge placed 3rd overall. Using the official metric for evaluation, we achieved an F1 score of 0.422 and 0.4289 on the validation and hold-out sets respectively.

翻译：冠状动脉造影仍是诊断冠状动脉疾病（全球最常见死因）的金标准。尽管该手术每年执行超过200万次，但能够快速、准确实现疾病自动检测及冠状动脉解剖定位的方法仍十分有限。本文提出了一种针对MICCAI 2023举办的基于X射线血管造影图像的自动区域冠状动脉疾病诊断（ARCADE）挑战赛的解决方案。对于动脉分割任务，我们采用三阶段方法：首先通过经典计算机视觉技术进行预处理与特征选择以增强血管对比度，随后基于YOLOv8构建集成模型生成血管候选图谱，最后采用基于逻辑的方法通过图排序重构冠脉树。我们的ARCADE挑战赛方案位列第三名。根据官方评估指标，我们在验证集和保留集上分别取得了0.422和0.4289的F1分数。