Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography

Cardiovascular disease has seriously affected the lives of modern people. One of the most commonly used imaging methods for diagnosing cardiovascular disease is computed tomography angiography (CTA). To generate a diagnosis report for doctors, every coronary artery needs to be identified and segment...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Zhang, Cheng-Jun, Xia, Denghui, Zheng, Chao, Wei, Jianyong, Cui, Yu, Qu, Yanzhen, Liao, Fangzhou
Format: Article
Language:English
Subjects:
Algorithms
Angiography
Arteries
Automatic identification
Blood
Computed tomography
Computed tomography angiography
Coronary artery
Datasets
Heart
Labels
Medical imaging
Myocardium
Physicians
Three-dimensional displays
Tomography
Veins & arteries
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Summary:Cardiovascular disease has seriously affected the lives of modern people. One of the most commonly used imaging methods for diagnosing cardiovascular disease is computed tomography angiography (CTA). To generate a diagnosis report for doctors, every coronary artery needs to be identified and segmented, including the right coronary artery (RCA), the posterior descending artery (PDA), the posterior lateral branch (PLB), the left circumflex (LCx), the left anterior descending branch (LAD), the ramus intermedius (RI), the obtuse marginal branches (OM1, OM2), and the diagonal branches (D1, D2). In this paper, we proposed a coronary artery automatic identification algorithm, which performs better in terms of accuracy than other similar algorithms and works efficiently. Normally, each Coronary Computed Tomographic Angiography (CCTA) dataset can be completed within seconds. This algorithm fully complies with the coronary label standard established by the Society of Cardiovascular Computed Tomography (SCCT). This algorithm has been put into operation in more than 100 hospitals for over one year. According to all previous tests, the labels obtained from the algorithm were compared with results manually corrected by several experts. Among 892 CCTA datasets, 95.96% of the labels obtained from the algorithms were correct.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2985416