Vessel wall segmentation of common carotid artery via multi-branch light network

Haochen Tan; Huimin Shi; Mingquan Lin; J. David Spence; Kwok-Leung Chan; Bernard Chiu

doi:10.1117/12.2549599

10 March 2020 Vessel wall segmentation of common carotid artery via multi-branch light network

Haochen Tan, Huimin Shi, Mingquan Lin, J. David Spence, Kwok-Leung Chan, Bernard Chiu

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131311 (2020) https://doi.org/10.1117/12.2549599
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Haochen Tan, Huimin Shi, Mingquan Lin, J. David Spence, Kwok-Leung Chan, and Bernard Chiu "Vessel wall segmentation of common carotid artery via multi-branch light network", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131311 (10 March 2020); https://doi.org/10.1117/12.2549599

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