13 March 2013 Semiautomatic segmentation of atherosclerotic carotid artery lumen using 3D ultrasound imaging
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86694A (2013) https://doi.org/10.1117/12.2007030
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
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 and 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 and 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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Md. Murad Hossain, Md. Murad Hossain, Khalid AlMuhanna , Khalid AlMuhanna , Limin Zhao, Limin Zhao, Brajesh Lal, Brajesh Lal, Siddhartha Sikdar, Siddhartha Sikdar, } "Semiautomatic segmentation of atherosclerotic carotid artery lumen using 3D ultrasound imaging", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86694A (13 March 2013); doi: 10.1117/12.2007030; https://doi.org/10.1117/12.2007030
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