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Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm
Rodrigo Dalvit Carvalho da Silva1,2, Ramin Soltanzadeh1,2,3, Chase R Figley1,2,3
1Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Insights
A new 3D method accurately tracks coronary artery centerlines from medical images. This automated approach achieves high precision, aiding in faster and more reliable detection of coronary artery disease.
Area of Science:
- Medical Imaging
- Computational Anatomy
- Cardiovascular Disease Research
Background:
- Coronary artery disease (CAD) is a major global health concern, necessitating advanced diagnostic tools.
- Medical imaging, particularly coronary artery computed tomography, plays a crucial role in CAD detection.
- Automated extraction of coronary artery features, such as centerlines, can enhance diagnostic accuracy and timeliness.
Purpose of the Study:
- To validate and benchmark a novel automated 3D centerline extraction method for coronary arteries.
- To assess the performance of the 3D divide and conquer Voronoi diagram method using a standardized dataset.
Main Methods:
- Utilized synthetically segmented coronary artery models from the Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset.
- Employed a 3D divide and conquer Voronoi diagram approach for automated centerline tracking.
- Evaluated performance using standard accuracy metrics against ground truth centerlines.
Main Results:
- The 3D method achieved exceptional accuracy, with an average overlap accuracy (OV) of 99.97% and an average error distance (AI) of 0.13 mm.
- High accuracy was consistent across all four coronary artery sub-types, including right coronary arteries (OV 99.99%), left anterior descending arteries (OV 100%), and left circumflex arteries (OV 99.96%).
- The method demonstrated excellent performance in tracking all 32 coronary vessel branches within the dataset.
Conclusions:
- The proposed 3D divide and conquer Voronoi diagram method accurately and automatically extracts coronary artery centerlines.
- This validated method shows significant potential for improving the speed and precision of cardiovascular diagnostics.
- Further exploration of this automated technique is warranted due to its high performance and clinical relevance.
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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