Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images
Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories fr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6087 |
| _version_ | 1827681344312836096 |
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| author | Byunghwan Jeon |
| author_facet | Byunghwan Jeon |
| author_sort | Byunghwan Jeon |
| collection | DOAJ |
| description | Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust ‘vesselness’ measurement is essential for extracting coronary centerlines. Importantly, we employed the DNN to robustly measure the vesselness using patch images, and we integrated softmax values to the likelihood function in our particle filtering framework. Tangent patches represent cross-sections of coronary arteries of circular shapes. Thus, 2D tangent patches are assumed to include enough features of coronary arteries, and the use of 2D patches significantly reduces computational complexity. Because coronary vasculature has multiple bifurcations, we also modeled a method to detect branching sites by clustering the particle locations. The proposed method is compared with three commercial workstations and two conventional methods from the academic literature. |
| first_indexed | 2024-03-10T07:14:48Z |
| format | Article |
| id | doaj.art-c3e66e288ddb4a029577ba55eb7c9b9b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:14:48Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c3e66e288ddb4a029577ba55eb7c9b9b2023-11-22T15:11:25ZengMDPI AGSensors1424-82202021-09-012118608710.3390/s21186087Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT ImagesByunghwan Jeon0School of Computer Science, Kyungil University, Gyeongsan 38428, KoreaExtraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust ‘vesselness’ measurement is essential for extracting coronary centerlines. Importantly, we employed the DNN to robustly measure the vesselness using patch images, and we integrated softmax values to the likelihood function in our particle filtering framework. Tangent patches represent cross-sections of coronary arteries of circular shapes. Thus, 2D tangent patches are assumed to include enough features of coronary arteries, and the use of 2D patches significantly reduces computational complexity. Because coronary vasculature has multiple bifurcations, we also modeled a method to detect branching sites by clustering the particle locations. The proposed method is compared with three commercial workstations and two conventional methods from the academic literature.https://www.mdpi.com/1424-8220/21/18/6087coronary arterydeep learningtrackingcomputed tomography |
| spellingShingle | Byunghwan Jeon Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images Sensors coronary artery deep learning tracking computed tomography |
| title | Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images |
| title_full | Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images |
| title_fullStr | Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images |
| title_full_unstemmed | Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images |
| title_short | Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images |
| title_sort | deep recursive bayesian tracking for fully automatic centerline extraction of coronary arteries in ct images |
| topic | coronary artery deep learning tracking computed tomography |
| url | https://www.mdpi.com/1424-8220/21/18/6087 |
| work_keys_str_mv | AT byunghwanjeon deeprecursivebayesiantrackingforfullyautomaticcenterlineextractionofcoronaryarteriesinctimages |