Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images.

Journal: Medical physics
PMID: 31071228

Abstract

PURPOSE: Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manual tracing to automated segmentations have also been investigated. Of those metrics, quantification of carotid plaques by measuring vessel-wall-volume (VWV) using the segmented media-adventitia (MAB) and lumen-intima (LIB) boundaries has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, semi-automatic MAB and LIB segmentation methods are required to help generate VWV measurements with high accuracy and less user interaction.

Authors

  • Ran Zhou
    Medical Ultrasound Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Aaron Fenster
    Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, London, Ontario N6A 5K8, Canada.
  • Yujiao Xia
    Medical Ultrasound Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • J David Spence
    Imaging Research Laboratories, Robarts Research Institute, Western University, London, ON, Canada.
  • Mingyue Ding
    Medical Ultrasound Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.

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