TY - JOUR
T1 - Centerline extraction of vasculature mesh
AU - Wei, Mingqiang
AU - Wang, Qiong
AU - Li, Yichen
AU - Pang, Wai Man
AU - Liang, Luming
AU - Wang, Jun
AU - Wong, Kelvin Kian Loong
AU - Abbott, Derek
AU - Qin, Jing
AU - Wu, Jianhuang
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61502137, Grant 61672510, and Grant 61233012, in part by the China Postdoctoral Science Foundation under Grant 2016M592047, in part by the Shenzhen Science and Technology Program under Grant JSGG20150602143414338 and Grant JCYJ20160429190300857, and in part by the Guangdong Science and Technology Program under Grant 2016A020220016.
Publisher Copyright:
© 2013 IEEE.
PY - 2018/2/7
Y1 - 2018/2/7
N2 - Mesh representation of vasculature is fundamental to many medical applications. The benefit is a clean and tidy appearance in terms of visualization, as well as the possibility of applying computer-assisted intervention and preoperative planning for patients. A vasculature mesh is often reconstructed by iso-surfacing its segmented volume data. Clinicians are usually interested in both the vasculature and its centerline. In this paper, we introduce a mesh centerline extraction approach in the case that volume data are unavailable. The extraction method is inspired by an observation that the vasculature is generally composed of piecewise cylindrical shapes. This observation leads to a conceptually simple but effective strategy to tackle the challenging problem of vasculature centerline extraction, which gracefully combines a branch segmentation scheme and a series of advanced techniques in discrete geometry processing. Our method competes favorably with three state-of-the-art methods in the completeness and accuracy of the extracted centerlines from real human vessels, including the pathological vasculature. Our method also usually leads to maximal and mean extraction errors of less than 1% and 0.5%, respectively.
AB - Mesh representation of vasculature is fundamental to many medical applications. The benefit is a clean and tidy appearance in terms of visualization, as well as the possibility of applying computer-assisted intervention and preoperative planning for patients. A vasculature mesh is often reconstructed by iso-surfacing its segmented volume data. Clinicians are usually interested in both the vasculature and its centerline. In this paper, we introduce a mesh centerline extraction approach in the case that volume data are unavailable. The extraction method is inspired by an observation that the vasculature is generally composed of piecewise cylindrical shapes. This observation leads to a conceptually simple but effective strategy to tackle the challenging problem of vasculature centerline extraction, which gracefully combines a branch segmentation scheme and a series of advanced techniques in discrete geometry processing. Our method competes favorably with three state-of-the-art methods in the completeness and accuracy of the extracted centerlines from real human vessels, including the pathological vasculature. Our method also usually leads to maximal and mean extraction errors of less than 1% and 0.5%, respectively.
KW - Centerline extraction
KW - Discrete geometry processing
KW - Interventional radiology simulation
KW - Rotational symmetry axis
KW - Vasculature mesh
KW - Vasculature segmentation
UR - http://www.scopus.com/inward/record.url?scp=85041533341&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2802478
DO - 10.1109/ACCESS.2018.2802478
M3 - Journal article
AN - SCOPUS:85041533341
SN - 2169-3536
VL - 6
SP - 10257
EP - 10268
JO - IEEE Access
JF - IEEE Access
ER -