Automated identification and grading of coronary artery stenoses with X-ray angiography. (December 2018)

• Record Type: Journal Article
• Authors: Wan, Tao; Feng, Hongxiang; Tong, Chao; Li, Deyu; Qin, Zengchang
• Is Part Of: Computer methods and programs in biomedicine. Volume 167(2018)
• Page Start: 13
• Page End: 22
• Publication Date: 2018-12
• Subjects: Automatic stenosis quantification -- Vessel diameter measurement -- Coronary artery stenosis -- X-ray angiography; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.10.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automated vessel stenosis quantification method in angiography images is presented. An improved measure of match method provides an accurate vessel diameter measure. The method can serve as a generalized framework to handle different image modalities. Abstract: Background and Objective: X-ray coronary angiography (XCA) remains the gold standard imaging technique for the diagnosis and treatment of cardiovascular disease. Automatic detection and grading of coronary stenoses in XCA are challenging problems due to the complex overlap of different background structures with intensity inhomogeneities. We present a new computerized image based method to accurately identify and quantify the stenosis severity on XCA. Methods: A unified framework, consisting of Hessian-based vessel enhancement, level-set skeletonization, improved measure of match measurement, and local extremum identification, is developed to distinctly reveal the vessel structures and accurately determine the stenosis grades. The methodology was validated on 143 consecutive patients who underwent diagnostic XCA through both qualitative and quantitative evaluations. Results: The presented algorithm was tested on a set of 267 vessel segments annotated by two expert cardiologists. The experimental results show that the method can effectively localize and quantify the vessel stenoses, achieving average detection accuracy, sensitivity, specificity, and F-score of 93.93%, 91.03%, 93.83%, 89.18%, respectively. Conclusions: A fully automatic coronary analysis method is devised for vessel stenosis detection and grading in XCA. The presented approach can potentially serve as a generalized framework to handle different image modalities.