Bonanno Lilla, Sottile Fabrizio, Ciurleo Rosella, Di Lorenzo Giuseppe, Bruschetta Daniele, Bramanti Alessia, Ascenti Giorgio, Bramanti Placido, Marino Silvia
IRCCS Centro Neurolesi "Bonino-Pulejo," Messina, Italy.
Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
J Stroke Cerebrovasc Dis. 2017 Feb;26(2):411-416. doi: 10.1016/j.jstrokecerebrovasdis.2016.09.045. Epub 2016 Oct 31.
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.
We considered 44 subjects, 22 with and 22 without the presence of plaques in the carotid axis, and we applied the snake algorithm.
The resulting interclass correlation coefficients (ICCs) were significant for all 3 parameters (mean echogenicity: ICC = .78 [95%CI: .55-0.90]; perimeter: ICC = .81 [95%CI: .61-0.92]; area: ICC = .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%.
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.
颈动脉粥样硬化是中风的主要原因之一。内膜中层厚度的测定、颈动脉粥样硬化斑块的识别以及不同狭窄程度的分类被视为评估动脉粥样硬化疾病的重要参数。这项工作的目的是分割斑块并更好地理解颈动脉粥样硬化。
我们纳入了44名受试者，其中22名颈动脉轴有斑块，22名没有斑块，并应用了蛇形算法。
所有3个参数的组间相关系数（ICC）均具有显著性（平均回声强度：ICC = 0.78 [95%CI：0.55 - 0.90]；周长：ICC = 0.81 [95%CI：0.61 - 0.92]；面积：ICC = 0.89 [95%CI：0.75 - 0.95]）。诊断准确率为82%，合适的截断值为224.5，灵敏度为79%，特异度为85%。
在本研究中，我们开发了一种自动识别颈动脉斑块的方法。我们的结果表明，图像分割自动系统可用于识别、表征和测量动脉粥样硬化性颈动脉斑块。
