Ordered multi-path propagation for vessel centerline extraction
Tao Han1, Danni Ai1, Ruirui An1
1Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
Insights
This study introduces a new method for extracting vessel centerlines from X-ray angiography images, crucial for diagnosing coronary artery disease. The approach ensures accurate and continuous vessel structure analysis, improving diagnostic capabilities.
Area of Science:
- Medical Imaging
- Computational Anatomy
- Cardiovascular Diagnostics
Background:
- Accurate vessel centerline extraction from X-ray angiography is vital for analyzing coronary artery disease.
- Challenges include image noise, poor contrast, and complex vessel structures, hindering complete and continuous extraction.
Purpose of the Study:
- To propose an iterative multi-path search framework for automatic and accurate vessel centerline extraction.
- To address limitations in current methods for analyzing vessel structures in coronary artery disease diagnosis.
Main Methods:
- Detection and confidence-based sorting of vessel seed points.
- Multi-path wavefront propagation and accumulated voting for centerline search, including multi-bifurcations.
- Iterative piecewise extension using wavefront propagation and keypoint detection.
Main Results:
- The method was evaluated on 1260 synthetic and 50 clinical angiography images.
- Achieved a high F1 score of 87.8% ± 2.7% on clinical angiography images.
- Demonstrated accurate and continuous vessel centerline extraction results.
Conclusions:
- The proposed iterative multi-path search framework effectively extracts vessel centerlines from X-ray angiography.
- The method overcomes challenges of noise, contrast, and complexity, providing reliable results for cardiovascular diagnostics.
Abstract:
Vessel centerline extraction from x-ray angiography images is essential for vessel structure analysis in the diagnosis of coronary artery disease. However, complete and continuous centerline extraction remains a challenging task due to image noise, poor contrast, and complexity of vessel structure. Thus, an iterative multi-path search framework for automatic vessel centerline extraction is proposed. First, the seed points of the vessel structure are detected and sorted by confidence. With the ordered seed points, multi-bifurcation centerline is searched through multi-path propagation of wavefront and accumulated voting. Finally, the centerline is further extended piecewise by wavefront propagation on the basis of keypoint detection. The latter two steps are performed alternately to obtain the final centerline result. The proposed method is qualitatively and quantitatively evaluated on 1260 synthetic images and 50 clinical angiography images. The results demonstrate that our method has a highF1score of 87.8% ± 2.7% for the angiography images and achieves accurate and continuous results of vessel centerline extraction.
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