Semiautomatic segmentation of atherosclerotic carotid artery lumen using 3D ultrasound imaging

被引:2
|
作者
Hossain, Md. Murad [1 ]
AlMuhanna, Khalid [1 ]
Zhao, Limin
Lal, Brajesh
Sikdar, Siddhartha [1 ]
机构
[1] George Mason Univ, Dept Elect & Comp Engn, Fairfax, VA 22030 USA
来源
关键词
Level set method; 3D segmentation; stopping criteria; ultrasound; carotid artery; lumen intima boundary; Hausdorff distance; stroke; 3-DIMENSIONAL ULTRASOUND; IMAGES; PLAQUE; ALGORITHMS; EVOLUTION; DISTANCE; VOLUME; TOOL;
D O I
10.1117/12.2007030
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Carotid atherosclerosis is a major cause of stroke. Imaging and monitoring plaque progression in 3D can better classify disease severity and potentially identify plaque vulnerability to rupture. In this study we propose to validate a new semiautomatic carotid lumen segmentation algorithm based on 3D ultrasound imaging that is designed to work in the presence of poor boundary contrast and complex 3D lumen geometries. Our algorithm uses a distance regularized level set evolution with a novel initialization and stopping criteria to localize the lumen-intima boundary (LIB). The external energy used in the level set method is a combination of region-based and edge-based energy. Initialization of LIB segmentation is first done in the longitudinal slice where the geometry of the carotid bifurcation is best visualized and then reconstructed in the cross sectional slice to guide the 3D initialization. Manual initialization of the contour is done only on the starting slice of the common carotid, bifurcation, and internal & external carotid arteries. Initialization of the other slices is done by eroding segmentation of previous slices. The user also initializes the boundary points for every slice. A combination of changes in the modified Hausdorff distance (MHD) between contours at successive iterations and a stopping boundary formed from initial boundary points is used as a stopping criterion to avoid over- or under-segmentation The proposed algorithm is evaluated against manually segmented boundaries by calculating dice similarity coefficient (DSC), HD and MHD in the common carotid (C), carotid bulb (B) and internal carotid (I) regions to get a better understanding of accuracy?. Results from five subjects with >50% carotid stenosis showed good agreement with manual segmentation; between the semiautomatic algorithm & manuals: DSC (C: 86.49 +/- 9.38, B: 82.21 +/- 8.49, I: 78.96 +/- 7.55), MHD (C: 3.79 +/- 1.64, B: 4.09 +/- 1.71, I: 4.12 +/- 2.01), HD (C: 8.07 +/- 2.59, B: 10.09 +/- 3.95, I: 11.28 +/- 5.06); and inter observers: DSC (C: 88.31 +/- 5, B: 82.45 +/- 7.57, I: 82.03 +/- 8.83), MHD (C: 3.77 +/- 2.09, B: 4.32 +/- 1.88, I: 4.56 +/- 2.24), HD (C: 7.61 +/- 2.67, B: 10.22 +/- 4.30, I: 10.63 +/- 4.94). This method is a first step towards achieving full 3D characterization of plaque progression, and is currently being evaluated in a longitudinal study of asymptomatic carotid stenosis
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页数:8
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