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Dalvit Carvalho da Silva R, Soltanzadeh R, Figley CR. Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm. J Imaging 2023; 9:268. [PMID: 38132686 PMCID: PMC10743762 DOI: 10.3390/jimaging9120268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023] Open
Abstract
Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features (e.g., vessel centerlines, cross-sectional areas along vessel branches, etc.) that may ultimately be able to assist with more accurate and timely diagnoses. The current study therefore validated and benchmarked a recently developed automated 3D centerline extraction method for coronary artery centerline tracking using synthetically segmented coronary artery models based on the widely used Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset. Based on standard accuracy metrics and the ground truth centerlines of all 32 coronary vessel branches in the RCAAEF training dataset, this 3D divide and conquer Voronoi diagram method performed exceptionally well, achieving an average overlap accuracy (OV) of 99.97%, overlap until first error (OF) of 100%, overlap of the clinically relevant portion of the vessel (OT) of 99.98%, and an average error distance inside the vessels (AI) of only 0.13 mm. Accuracy was also found to be exceptionally for all four coronary artery sub-types, with average OV values of 99.99% for right coronary arteries, 100% for left anterior descending arteries, 99.96% for left circumflex arteries, and 100% for large side-branch vessels. These results validate that the proposed method can be employed to quickly, accurately, and automatically extract 3D centerlines from segmented coronary arteries, and indicate that it is likely worthy of further exploration given the importance of this topic.
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Affiliation(s)
- Rodrigo Dalvit Carvalho da Silva
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
- Division of Diagnostic Imaging, Health Sciences Centre, Shared Health Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Ramin Soltanzadeh
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
- Division of Diagnostic Imaging, Health Sciences Centre, Shared Health Manitoba, Winnipeg, MB R3T 2N2, Canada
- Biomedical Engineering Program, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Chase R. Figley
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
- Division of Diagnostic Imaging, Health Sciences Centre, Shared Health Manitoba, Winnipeg, MB R3T 2N2, Canada
- Biomedical Engineering Program, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
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Fu Z, Fu Z, Fang Z, Wang Z, Fei J, Xie R, Han H. Prior skeleton based online deep reinforcement learning for coronary artery centerline extraction. Proc Inst Mech Eng H 2023:9544119231167926. [PMID: 37052174 PMCID: PMC10102823 DOI: 10.1177/09544119231167926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
Coronary centerline extraction is an essential technique for X-ray coronary angiography (XCA) image analysis, which provides qualitative and quantitative guidance for percutaneous coronary intervention (PCI). In this paper, an online deep reinforcement learning method for coronary centerline extraction is proposed based on the prior vascular skeleton. Firstly, with XCA image preprocessing (foreground extraction and vessel segmentation) results, the improved ZhangSuen image thinning algorithm is used to rapidly extract the preliminary vascular skeleton network. On this basis, according to the spatial-temporal and morphological continuity of the angiography image sequence, the connectivity of different branches is determined using k-means clustering, and the vessel segments are then grouped, screened, and reconnected to obtain the aorta and its major branches. Finally, using the previous results as prior information, an online Deep Q-Network (DQN) reinforcement learning method is proposed to optimize each branch simultaneously. It comprehensively considers grayscale intensity and eigenvector continuity to achieve the combination of data-driven and model-driven without pre-training. Experimental results on clinical images and the third-party dataset demonstrate that the proposed method can accurately extract, restructure, and optimize the centerline of XCA images with a higher overall accuracy than the existing state-of-the-art methods.
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Affiliation(s)
- Zeyu Fu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Zhuang Fu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Zi Fang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Zehao Wang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Fei
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University, Shanghai, China
- Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Rongli Xie
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Han
- Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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