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.

我们介绍了一种用于心血管狭窄分割的半监督方法，通过数据增广和利用伪标签的学习技术，实现了在冠状动脉造影图像中准确评估病患情况的自动诊断算法。

SSASS：半监督式狭窄分割方法