SSASS: Semi-Supervised Approach for Stenosis Segmentation
This provides an automated solution for medical practitioners to assess stenosis severity from medical imaging, though it appears incremental as it builds on existing semi-supervised techniques.
The paper tackles the challenge of precisely identifying coronary artery stenosis in X-ray angiography images by introducing a semi-supervised approach with tailored data augmentation and pseudo-label learning, achieving exceptional performance in the ARCADE challenge using a single model instead of an ensemble.
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.