Coronary artery stenosis is a critical health risk, and its precise identification in Coronary Angiography (CAG) can significantly aid medical practitioners in accurately evaluating the severity of a patient's condition. The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task. To tackle these obstacles, we introduce a semi-supervised approach for cardiovascular stenosis segmentation. Our strategy begins with data augmentation, specifically tailored to replicate the structural characteristics of coronary arteries. We then apply a pseudo-label-based semi-supervised learning technique that leverages the data generated through our augmentation process. Impressively, our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) Stenosis Detection Algorithm challenge by utilizing a single model instead of relying on an ensemble of multiple models. This success emphasizes our method's capability and efficiency in providing an automated solution for accurately assessing stenosis severity from medical imaging data.

翻译：冠状动脉狭窄是一种严重的健康风险，在冠状动脉造影（CAG）中精确识别狭窄可显著帮助医疗从业者准确评估患者病情的严重程度。冠状动脉结构的复杂性以及X射线图像中固有的噪声给这一任务带来了巨大挑战。为克服这些障碍，我们提出了一种用于心血管狭窄分割的半监督方法。我们的策略始于数据增强，该增强专门设计用于复制冠状动脉的结构特征。随后，我们应用一种基于伪标签的半监督学习技术，该技术利用通过增强过程生成的数据。令人瞩目的是，我们的方法在基于区域的自动冠状动脉疾病诊断X射线血管造影图像（ARCADE）狭窄检测算法挑战中展现了卓越性能，仅使用单一模型而非依赖多模型集成。这一成功强调了我们的方法在提供自动化解决方案、从医学影像数据中准确评估狭窄严重程度方面的能力和效率。