Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive angiography (ICA) is important for stenosis assessment and disease (CAD) diagnosis. In this paper, we propose a multi-step algorithm based on analyzing arterial segments extracted from ICAs. The proposed firstly extracts the entire binary mask (binary vascular tree) deep learning-based method. Then extract centerline tree separate it into different segments. Finally, by extracting underlying topology, position, pixel features, construct powerful segment classifier support vector machine. Each classified left (LCA), anterior descending (LAD), other types method was tested dataset with 225 ICAs achieved mean accuracy 70.33% multi-class classification intersection over union 0.6868 arteries. experimental results show effectiveness algorithm, which provides impressive performance individual arteries in