@inproceedings{e2faa325c92a4003855aa534af886750,
title = "Gradient vector flow field and fast marching based method for centerline computation of coronary arteries",
abstract = "This paper develops new concept of validating centerline extraction method of coronary arteries. The approach is based on the gradient vector flow (GVF) filed of the 3D segmented coronary arteries models. It is implemented with the Gaussian based speed image. The approach was validated over 3 three-dimensional synthetic vessel models and further tested in 3 clinical coronary arteries models reconstructed from computed tomography coronary angiography (CTCA) in human patients. The results showed an excellent agreement between the proposed method and ground truth centerline in synthetic vessel models. Second, the proposed method was applicable in both left coronary arteries and right coronary arteries with average processing time of 25.7 min per case. In conclusion, the proposed gradient vector flow field and fast marching based method should have more routine clinical applicability.",
keywords = "Coronary centerline, Fast marching method, Gradient vector flow",
author = "Hengfei Cui and Yong Xia",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017 ; Conference date: 22-09-2017 Through 23-09-2017",
year = "2017",
doi = "10.1007/978-3-319-67777-4_54",
language = "英语",
isbn = "9783319677767",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "597--607",
editor = "Yi Sun and Huchuan Lu and Lihe Zhang and Jian Yang and Hua Huang",
booktitle = "Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings",
}