Experimental Assessment of YOLO Variants for Coronary Artery Disease Segmentation from Angiograms

Coronary artery disease (CAD) is one of the leading causes of mortality worldwide, highlighting the importance of developing accurate and efficient diagnostic tools. This study presents a comparative evaluation of three recent YOLO architecture versions (YOLOv8, YOLOv9, and YOLOv11) for the tasks of coronary vessel segmentation and stenosis detection using the ARCADE dataset. Two workflows were explored: one with original angiographic images and another incorporating Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement. Models were trained for 100 epochs using the AdamW optimizer and evaluated with precision, recall, and F1-score under a pixel-based segmentation framework. YOLOv9-E achieved the highest performance in vessel segmentation with an F1-score of 0.4524, while YOLOv11-X was most effective for stenosis detection, achieving an F1-score of 0.7826. Although CLAHE improved local contrast, it did not consistently improve segmentation results and occasionally introduced artifacts that negatively affected model performance. Compared to state-of-the-art methods, the YOLO models demonstrated competitive results, especially for large, well-defined coronary segments, but showed limitations in detecting smaller or more complex pathological structures. These findings support the use of YOLO-based architectures for real-time CAD segmentation tasks and highlight opportunities for future improvement through the integration of attention mechanisms or hybrid deep learning strategies.