Ultrasound carotid plaque video segmentation

Published on Jan 31, 2018
· DOI :10.1049/PBHE013E_ch20
Chritos P. Loizou18
Estimated H-index: 18
(CUT: Cyprus University of Technology),
Constantinos S. Pattichis34
Estimated H-index: 34
(UCY: University of Cyprus)
Border identification of the atherosclerotic carotid plaque, the common carotid artery (CCA), degree of stenosis, as well as the characteristics of the arterial wall (plaque size, composition and elasticity), may add additional clinical information for the assessment of future cardiovascular events. We propose and evaluate in this chapter an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA. The system is based on video frame normalization, speckle reduction filtering, M-mode-state-based identification, parametric active contours and snake's segmentation. The cardiac cycle in each video is first identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is segmented for a time period of at least one full cardiac cycle by initializing the algorithm in the first video frame. Human manual assistance may be provided if needed. The atherosclerotic plaque borders are tracked and segmented in the subsequent frames. We also propose an initialization method for positioning the snake as close as possible to the plaque borders, based on morphology operators, where initial contours are estimated every 20 video frames. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared to the manual segmentations of an expert, available every 20 frames in a time span of 3-5 s, covering in general two cardiac cycles. The segmentation results were very promising, according to the expert objective evaluation, with a true-negative fraction (TNF) specificity of 83.7% + 7.6%, a true-positive fraction (TPF) sensitivity of 85.42% + 8.1%, between the ground truth and the proposed segmentation method, a kappa index (KI) of 84.6% and an overlap index (OI) of 74.7%. We also computed the cardiac state identification for the CCA. It is shown that the integrated system presented in this chapter can be used for the video segmentation of the CCA plaque in ultrasound videos.
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