Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images

In current medical imaging, coronary artery stenosis quantification requires fast and accurate coronary centerline computation. This paper develops a new framework for extracting coronary centerlines from 3-D segmented coronary arteries models. The approach utilizes the gradient vector flow (GVF) fi...

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Main Authors: Hengfei Cui, Yong Xia
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8419695/
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spelling doaj-b8f4ff41bd9e462e84493b8a82e960e12021-03-29T21:06:29ZengIEEEIEEE Access2169-35362018-01-016418164182610.1109/ACCESS.2018.28597868419695Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT ImagesHengfei Cui0https://orcid.org/0000-0001-8625-2521Yong Xia1Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaIn current medical imaging, coronary artery stenosis quantification requires fast and accurate coronary centerline computation. This paper develops a new framework for extracting coronary centerlines from 3-D segmented coronary arteries models. The approach utilizes the gradient vector flow (GVF) filed-based speed image of the vessel model and implements a wavefront propagation technique for centerline branch tracking. The approach was validated over 17 3-D synthetic vessel models. The results showed a good agreement between the proposed method and ground truth centerline in synthetic vessel models with an average distance of 0.25 mm and overlap measure of 96.0%, given the CT scans with a resolution of about 0.3 mm × 0.3mm × 0.4mm. Second, the proposed method was further tested in six clinical coronary arteries models reconstructed from computed tomography coronary angiography in human patients and found to be applicable in both left coronary arteries and right coronary arteries with an average processing time of 16 minutes per case. In conclusion, the proposed GVF field and the fast marching-based method should have more routine clinical applicability.https://ieeexplore.ieee.org/document/8419695/Computed tomography angiographycoronary centerlinefast marching methodgradient vector flowvessel segmentation
collection DOAJ
language English
format Article
sources DOAJ
author Hengfei Cui
Yong Xia
spellingShingle Hengfei Cui
Yong Xia
Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images
IEEE Access
Computed tomography angiography
coronary centerline
fast marching method
gradient vector flow
vessel segmentation
author_facet Hengfei Cui
Yong Xia
author_sort Hengfei Cui
title Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images
title_short Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images
title_full Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images
title_fullStr Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images
title_full_unstemmed Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images
title_sort automatic coronary centerline extraction using gradient vector flow field and fast marching method from ct images
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2018-01-01
description In current medical imaging, coronary artery stenosis quantification requires fast and accurate coronary centerline computation. This paper develops a new framework for extracting coronary centerlines from 3-D segmented coronary arteries models. The approach utilizes the gradient vector flow (GVF) filed-based speed image of the vessel model and implements a wavefront propagation technique for centerline branch tracking. The approach was validated over 17 3-D synthetic vessel models. The results showed a good agreement between the proposed method and ground truth centerline in synthetic vessel models with an average distance of 0.25 mm and overlap measure of 96.0%, given the CT scans with a resolution of about 0.3 mm × 0.3mm × 0.4mm. Second, the proposed method was further tested in six clinical coronary arteries models reconstructed from computed tomography coronary angiography in human patients and found to be applicable in both left coronary arteries and right coronary arteries with an average processing time of 16 minutes per case. In conclusion, the proposed GVF field and the fast marching-based method should have more routine clinical applicability.
topic Computed tomography angiography
coronary centerline
fast marching method
gradient vector flow
vessel segmentation
url https://ieeexplore.ieee.org/document/8419695/
work_keys_str_mv AT hengfeicui automaticcoronarycenterlineextractionusinggradientvectorflowfieldandfastmarchingmethodfromctimages
AT yongxia automaticcoronarycenterlineextractionusinggradientvectorflowfieldandfastmarchingmethodfromctimages
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