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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9056467/ |
| _version_ | 1818608519484538880 |
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| author | Cheng-Jun Zhang Denghui Xia Chao Zheng Jianyong Wei Yu Cui Yanzhen Qu Fangzhou Liao |
| author_facet | Cheng-Jun Zhang Denghui Xia Chao Zheng Jianyong Wei Yu Cui Yanzhen Qu Fangzhou Liao |
| author_sort | Cheng-Jun Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T14:43:56Z |
| format | Article |
| id | doaj.art-e058467edf9547c09d7bc33f06cbfb06 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T14:43:56Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e058467edf9547c09d7bc33f06cbfb062022-12-21T22:27:50ZengIEEEIEEE Access2169-35362020-01-018655666557210.1109/ACCESS.2020.29854169056467Automatic Identification of Coronary Arteries in Coronary Computed Tomographic AngiographyCheng-Jun Zhang0https://orcid.org/0000-0002-4458-5843Denghui Xia1Chao Zheng2Jianyong Wei3Yu Cui4Yanzhen Qu5Fangzhou Liao6School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaYukun Beijing Network Technology Company Ltd., Beijing, ChinaYukun Beijing Network Technology Company Ltd., Beijing, ChinaYukun Beijing Network Technology Company Ltd., Beijing, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaYukun Beijing Network Technology Company Ltd., Beijing, ChinaCardiovascular 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.https://ieeexplore.ieee.org/document/9056467/Automatic identificationcomputed tomography angiographycoronary artery |
| spellingShingle | Cheng-Jun Zhang Denghui Xia Chao Zheng Jianyong Wei Yu Cui Yanzhen Qu Fangzhou Liao Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography IEEE Access Automatic identification computed tomography angiography coronary artery |
| title | Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography |
| title_full | Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography |
| title_fullStr | Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography |
| title_full_unstemmed | Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography |
| title_short | Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography |
| title_sort | automatic identification of coronary arteries in coronary computed tomographic angiography |
| topic | Automatic identification computed tomography angiography coronary artery |
| url | https://ieeexplore.ieee.org/document/9056467/ |
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