Artery Vein

Artery-vein segmentation, crucial for diagnosing various diseases and surgical planning, is a rapidly advancing field leveraging advanced image analysis techniques. Current research focuses on developing robust automated segmentation methods, employing deep learning architectures and graph-based approaches to accurately delineate arteries and veins from medical images like CT scans and retinal fundus photographs, often incorporating topology-preserving constraints to improve accuracy. These improvements enable more precise measurements of vascular features (e.g., artery-vein ratio, tortuosity) and facilitate non-invasive assessments of vascular health, ultimately impacting clinical diagnosis and physiological understanding.