Abstract
Automatic centerline extraction based on 3D coronary artery segmentation results is a very important step before quantitative evaluation of intravascular lumen cross-section. In this paper, a method based on the combination of fast marching and gradient vector flow (GVF) is proposed to extract the centerline of the complete coronary artery tree in 3D angiographic images. With the centerline of blood vessel, we propose an automatic method to extract the cross-section of blood vessel lumen. This method calculates the tangent vector based on the two adjacent centerline points before and after the midline point, and then calculates the cross-sectional equation through the centerline point, and then obtains the cross-sectional contour of the cross-section and the surface mesh of blood vessel. The new method is designed to extract the cross-section of 3D intravascular lumen in real physical coordinates, which avoids the traditional interpolation processing in pixel coordinates and improves the accuracy of cross-section extraction. Given the accuracy and efficiency, the proposed coronary artery lumen area measurement algorithm can facilitate quantitative assessment of the anatomic severity of coronary stenosis.
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Acknowledgment
The study was supported in part by the National Natural Science Foundation of China under Grants 61771397, 61801391, 61801393 and 61801395, in part by the Natural Science Basic Research Project in Shaanxi of China (Program No. 2019JQ-254 and 2019JQ-158), and in part by the Fundamental Research Funds for the Central Universities under Grants 3102018zy031.
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Cui, H., Xia, Y., Zhang, Y. (2019). Three-Dimensional Coronary Artery Centerline Extraction and Cross Sectional Lumen Quantification from CT Angiography Images. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_20
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DOI: https://doi.org/10.1007/978-3-030-36189-1_20
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