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Segmentation of the Carotid Arteries from 3D Ultrasound Images

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Advanced Computational Approaches to Biomedical Engineering

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Abstract

Ultrasound (US) Doppler flow-velocity imaging has been used extensively in the diagnosis and management of carotid atherosclerosis. Doppler ultrasound-based measurement is a well-established screening tool for the assessment of stenosis severity. However, this method of measurement does not provide information on carotid plaque morphology, plaque vulnerability, or composition. In addition to stenosis severity, US-based phenotypes of carotid atherosclerosis include intima–media thickness (IMT), and total plaque area (TPA). More recently, vessel-wall-volume (VWV) and total-plaque-volume (TPV) have emerged as sensitive and useful US phenotypes of carotid atherosclerosis that measure plaque burden in 3D images. In order to accelerate the translation of these 3D US-based carotid atherosclerosis measurements to clinical practice, semiautomated methods of measurement are required to enable multiple observers to be trained in shorter time periods and with decreased inter-observer variability. This has stimulated investigators to develop accurate and robust segmentation algorithms allowing efficient quantification of carotid atherosclerosis. In this chapter, we demonstrate that 3D US is a viable technique for quantifying the progression and regression of carotid atherosclerosis and describe algorithms for segmentation of carotid vessels.

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Acknowledgments

The authors acknowledge the financial support from the Canadian Institutes of Health Research (CIHR) and the Ontario Research Fund (ORF) program. E. Ukwatta acknowledges the support from the Natural Sciences and Engineering Research Council of Canada (NSERC) Canada Graduate Scholarship (CGS). A. Fenster holds a Canada Research Chair in Biomedical Engineering, and acknowledges the support of the Canada Research Chair Program.

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Authors and Affiliations

  1. Imaging Research Laboratories, Robarts Research Institute, Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada, N6A 3K7

    Eranga Ukwatta

  2. Imaging Research Laboratories, Robarts Research Institute, Biomedical Engineering Graduate Program, Medical Biophysics, University of Western Ontario, London, ON, Canada, N6A 3K7

    Aaron Fenster

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  1. Eranga Ukwatta
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  2. Aaron Fenster
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Correspondence to Eranga Ukwatta .

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  1. Dept. of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA

    Punam K. Saha

  2. Dept. of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Ujjwal Maulik

  3. Dept. of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Subhadip Basu

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Ukwatta, E., Fenster, A. (2014). Segmentation of the Carotid Arteries from 3D Ultrasound Images. In: Saha, P., Maulik, U., Basu, S. (eds) Advanced Computational Approaches to Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41539-5_6

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