  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.
