Coronary angiography continues to serve as the primary method for diagnosing coronary artery disease (CAD), which is the leading global cause of mortality. The severity of CAD is quantified by the location, degree of narrowing (stenosis), and number of arteries involved. In current practice, this quantification is performed manually using visual inspection and thus suffers from poor inter- and intra-rater reliability. The MICCAI grand challenge: Automatic Region-based Coronary Artery Disease diagnostics using the X-ray angiography imagEs (ARCADE) curated a dataset with stenosis annotations, with the goal of creating an automated stenosis detection algorithm. Using a combination of machine learning and other computer vision techniques, we propose the architecture and algorithm StenUNet to accurately detect stenosis from X-ray Coronary Angiography. Our submission to the ARCADE challenge placed 3rd among all teams. We achieved an F1 score of 0.5348 on the test set, 0.0005 lower than the 2nd place.

翻译：冠状动脉造影仍是诊断冠状动脉疾病（CAD）的主要方法，该疾病是全球首要死因。CAD的严重程度通过病变位置、狭窄程度以及受累动脉数量进行量化。当前临床实践中，这一量化过程依赖人工目视检查，因此存在评估者间与评估者内部可靠性差的问题。MICCAI挑战赛：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）整理了一个含有狭窄标注的数据集，旨在开发自动狭窄检测算法。我们结合机器学习与其他计算机视觉技术，提出了StenUNet架构与算法，用于从X射线冠状动脉造影中准确检测狭窄。我们在ARCADE挑战赛中的提交结果在所有参赛队伍中位列第三。我们在测试集上取得了0.5348的F1分数，比第二名低0.0005。