Automatic Algorithm for Segmentation of Atherosclerotic Carotid Plaque
Lilla Bonanno1, Fabrizio Sottile2, Rosella Ciurleo1
1IRCCS Centro Neurolesi "Bonino-Pulejo," Messina, Italy.
Insights
This study presents an automatic method using image segmentation to identify and measure carotid plaques, aiding in stroke risk assessment. The system accurately characterizes atherosclerotic carotid plaques.
Area of Science:
- Medical Imaging
- Cardiovascular Research
- Stroke Prevention
Background:
- Carotid atherosclerosis is a primary cause of stroke.
- Intima-media thickness, plaque identification, and stenosis classification are key assessment parameters.
- Understanding carotid atherosclerosis aids in stroke risk stratification.
Purpose of the Study:
- To develop an automatic method for carotid plaque segmentation.
- To improve the comprehension and assessment of carotid atherosclerosis.
- To enhance the identification and characterization of atherosclerotic plaques.
Main Methods:
- Applied the snake algorithm for image segmentation.
- Studied 44 subjects (22 with and 22 without carotid plaques).
- Utilized image analysis to segment carotid plaques.
Main Results:
- Achieved 82% diagnostic accuracy for plaque identification.
- Demonstrated high interclass correlation coefficients for plaque parameters (echogenicity, perimeter, area).
- Reported sensitivity of 79% and specificity of 85% with a cutoff of 224.5.
Conclusions:
- An automatic image segmentation system can effectively identify carotid plaques.
- The developed method aids in characterizing and measuring atherosclerotic carotid plaques.
- This technology supports better assessment of atherosclerosis and stroke risk.
Background:
Carotid atherosclerosis is one of the major causes of stroke. The determination of the intima-media thickness, the identification of carotid atherosclerotic plaque, and the classification of the different stenoses are considered as important parameters for the assessment of atherosclerotic diseases. The aim of this work is to segment the plaques and to allow a better comprehension of carotid atherosclerosis.
Methods:
We considered 44 subjects, 22 with and 22 without the presence of plaques in the carotid axis, and we applied the snake algorithm.
Results:
The resulting interclass correlation coefficients (ICCs) were significant for all 3 parameters (mean echogenicity: ICC1 = .78 [95%CI: .55-0.90]; perimeter: ICC2 = .81 [95%CI: .61-0.92]; area: ICC3 = .89 [95%CI: .75-0.95]). The diagnostic accuracy was 82%, with an appropriate cutoff value of 224.5, sensitivity of 79%, and specificity of 85%.
Conclusions:
In this study, we developed an automatic method to identify the carotid plaque. Our results showed that an automatic system of image segmentation could be used to identify, characterize, and measure atherosclerotic carotid plaques.
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