Stenosis-YOLO: Semisupervised YOLOv8 with Edge Enhancement and Hierarchical Feature Fusion for Coronary Stenosis...

An early and accurate diagnosis of coronary artery disease (CAD) is essential for effective treatment. Although X-ray coronary angiography (XCA) is the clinical gold standard for CAD diagnosis, blurred vessel boundaries, low contrast, and minute stenotic regions can make pixel-level segmentation difficult. We propose Stenosis-YOLO, a YOLOv8-based segmentation framework that addresses these challenges. Its key contributions are as follows: (1) an edge enhancement stem (EES) that combines Laplacian edge extraction with spatial feature pooling to strengthen fine vascular and boundary representations; (2) a small-target-aware neck that uses space-to-depth convolution (SPD-Conv) on the P2 layer and a feature fusion module (FFM) to improve multiscale integration; and (3) a semisupervised pseudo-label self-training strategy that uses unlabeled data to improve performance. When evaluated on the ARCADE benchmark, Stenosis-YOLO significantly outperformed the state-of-the-art techniques for coronary stenosis segmentation and instance segmentation, achieving improvements of 6.4%, 4.9%, 5.7%, and 4.2% in terms of precision, recall, F1-score, and mean average precision (mAP), respectively, over the baseline YOLOv8 model. Stenosis-YOLO also performed exceptionally well in stenosis detection, achieving 0.976, 0.972, 0.974, and 0.983 for precision, recall, F1-score, and mAP, respectively. This represents enhancements of 1.6% and 0.7% in the F1-score and mAP, respectively, compared to the leading coronary stenosis detection model. These results demonstrate the effectiveness of combining edge enhancement, small-target modeling, and semisupervised learning for accurate coronary stenosis segmentation.