Segmentation of the Carotid Arteries from 3D Ultrasound Images (original) (raw)

2013, Advanced Computational Approaches to Biomedical Engineering

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.