Yaofang Liu1, Xinyue Zhang1, Wenlong Wan2, Shaoyu Liu2, Yingdi Liu1, Hu Liu3, Xueying Zeng1, Qing Zhang4. 1. School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China. 2. School of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, China. 3. School of Material Science and Engineering, Ocean University of China, Qingdao, Shandong, China. 4. Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Abstract
PURPOSE: Coronary angiography is the "gold standard" for diagnosing coronary artery disease. At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. METHODS: Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this method, the user can choose any vessel segment to do further analysis of its stenoses. RESULTS: Experiments show that the proposed methods are robust for angiograms with various vessel structures. The precision, sensitivity, and [Formula: see text] score of the automatic stenosis detection method are 0.821, 0.757, and 0.788, respectively. Further investigation proves that the interactive method can provide a more precise outcome of stenosis detection, and our quantitative analysis is closer to reality. CONCLUSION: The proposed automatic method and interactive method are effective and can complement each other in clinical practice. The first method can be used for preliminary screening, and the second method can be used for further quantitative analysis. We believe the proposed solution is more suitable for the clinical diagnosis of CAD.
PURPOSE: Coronary angiography is the "gold standard" for diagnosing coronary artery disease. At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. METHODS: Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this method, the user can choose any vessel segment to do further analysis of its stenoses. RESULTS: Experiments show that the proposed methods are robust for angiograms with various vessel structures. The precision, sensitivity, and [Formula: see text] score of the automatic stenosis detection method are 0.821, 0.757, and 0.788, respectively. Further investigation proves that the interactive method can provide a more precise outcome of stenosis detection, and our quantitative analysis is closer to reality. CONCLUSION: The proposed automatic method and interactive method are effective and can complement each other in clinical practice. The first method can be used for preliminary screening, and the second method can be used for further quantitative analysis. We believe the proposed solution is more suitable for the clinical diagnosis of CAD.