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Proceedings Paper

Vessel wall segmentation of common carotid artery via multi-branch light network
Author(s): Haochen Tan; Huimin Shi; Mingquan Lin; J. David Spence; Kwok-Leung Chan; Bernard Chiu
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Paper Abstract

Vessel wall volume (VWV) and local vessel-wall-plus-plaque thickness (VWT) measured from 3D ultrasound (3DUS) are sensitive to change of plaque burden over time and are useful in evaluating treatment effect. Segmentation of the media-adventitia (MAB) and lumen-intima boundaries (LIB) was required in VWV and VWT quantification. Manual segmentation of these boundaries is time-consuming and prone to observer variability. In this work, we developed and validated a method to segment MAB and LIB from axial images re-sliced from 3DUS images using a light-weight coarse-to-fine network. The proposed network is computationally efficient with only 0.59M parameters (compared to 31M parameters in U-Net). The boundaries segmented by the proposed algorithm were compared with manually segmented boundaries. The proposed algorithm attained Dice similarity coefficients (DSC) of 92:5±3:09% and 85:4±6:04% for MAB and LIB respectively, which are higher than those attained by U-Net family networks, including U-Net++, scaled U-Net and attention U-Net. This segmentation tool will facilitate efficient quantification of VWV and VWT, thereby making it more feasible for them to be measured in clinical trials evaluating treatment effect or for stroke risk stratification.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131311 (10 March 2020); doi: 10.1117/12.2549599
Show Author Affiliations
Haochen Tan, City Univ. of Hong Kong (Hong Kong, China)
Huimin Shi, City Univ. of Hong Kong (Hong Kong, China)
Mingquan Lin, City Univ. of Hong Kong (Hong Kong, China)
J. David Spence, Robarts Research Institute (Canada)
Kwok-Leung Chan, City Univ. of Hong Kong (Hong Kong, China)
Bernard Chiu, City Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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