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Atherosclerosis is a progressive disorder characterized by the buildup of plaques on the arterial inner wall, causing them to narrow and harden over time. These plaques comprise lipids, calcium, blood components, carbohydrates, and fibrous tissue. The process primarily affects the intima of large and medium-sized arteries, reducing blood flow in any artery.Etiology and risk factorsThe cause of atherosclerosis is multifactorial, involving a complex interplay among endothelial injury, lipid...
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Related Experiment Video

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A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
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Atherosclerotic carotid plaque segmentation.

C P Loizou1, C S Pattichis, R S H Istepanian

  • 1Dept. of Comput. Sci., Intercollege, Limassol, Cyprus.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
Summary

This study introduces an automated method for segmenting atherosclerotic carotid plaque in ultrasound images, improving accuracy and reducing manual correction needs for better cardiovascular disease assessment.

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Area of Science:

  • Medical Imaging
  • Cardiovascular Disease Research
  • Biomedical Engineering

Background:

  • Atherosclerosis, a leading cause of heart attack and stroke, necessitates accurate plaque characterization.
  • Ultrasound imaging is crucial for visualizing carotid artery plaque, but manual segmentation can be time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a computerized method for automated segmentation of atherosclerotic carotid plaque from ultrasound images.
  • To assess the accuracy and reproducibility of the automated method compared to expert manual delineations.

Main Methods:

  • Utilized blood flow imaging to establish an initial plaque contour.
  • Applied despeckle filtering and active contour models (snakes) for precise boundary fitting.
  • Validated the method on 35 longitudinal ultrasound images of carotid arteries.

Main Results:

  • The computerized method achieved high accuracy, with a true positive fraction (TPF) of 86.44% and a true negative fraction (TNF) of 84.03%.
  • False positive fraction (FPF) was 7% and false negative fraction (FNF) was 8.5%, indicating robust performance.
  • The automated segmentation required minimal to no manual correction in most cases, demonstrating excellent reproducibility.

Conclusions:

  • The developed computerized method provides a reliable and efficient tool for atherosclerotic carotid plaque segmentation.
  • This automated approach has the potential to enhance the diagnosis and monitoring of cardiovascular diseases.
  • Further integration into clinical workflows could improve efficiency and consistency in plaque analysis.