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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

View graph of relations

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationPROCEEDINGS OF SPIE
Subtitle of host publicationMedical Imaging 2020: Image Processing
EditorsIvana Išgum, Bennett A. Landman
PublisherSPIE
Number of pages6
Volume11313
ISBN (Electronic)9781510633940
ISBN (Print)9781510633933
Publication statusPublished - Feb 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Number50
Volume21
ISSN (Print)1605-7422

Conference

TitleSPIE Medical Imaging 2020
LocationMarriott Marquis Houston
PlaceUnited States
CityHouston, Texas
Period15 - 20 February 2020

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.

Research Area(s)

  • 3D ultrasound images, Common carotid artery (CCA), Light-weight CNN, Segmentation

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Vessel wall segmentation of common carotid artery via multi-branch light network. / Tan, Haochen; Shi, Huimin; Lin, Mingquan; David Spence, J.; Chan, Kwok-Leung; Chiu, Bernard.

PROCEEDINGS OF SPIE: Medical Imaging 2020: Image Processing. ed. / Ivana Išgum; Bennett A. Landman. Vol. 11313 SPIE, 2020. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 21, No. 50).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review