[1]贺桢,石蕴玉,刘翔,等.基于卷积神经网络检测颈动脉斑块[J].中国医学物理学杂志,2022,39(1):122-126.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.020]
　HE Zhen,SHI Yunyu,LIU Xiang,et al.Detection of carotid plaques based on convolutional neural network[J].Chinese Journal of Medical Physics,2022,39(1):122-126.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.020]
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基于卷积神经网络检测颈动脉斑块()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第1期
页码:
122-126
栏目:
医学人工智能
出版日期:
2022-01-17

文章信息/Info

Title:
Detection of carotid plaques based on convolutional neural network
文章编号:
1005-202X（2022）01-0122-05
作者:
贺桢1; 石蕴玉1; 刘翔1; 杨少玲2; 牛嘉丰1
1.上海工程技术大学电子电气工程学院， 上海 201620； 2.上海市第八人民医院超声医学科， 上海 200235
Author(s):
HE Zhen1;  SHI Yunyu1;  LIU Xiang1;  YANG Shaoling2;  NIU Jiafeng1
1. School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Ultrasound, Shanghai Eighth Peoples Hospital, Shanghai 200235, China
关键词:
卷积神经网络; 颈动脉斑块; 内中膜厚度; 超声图像
Keywords:
Keywords: convolutional neural network carotid plaque intima-media thickness ultrasound image
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2022.01.020
文献标志码:
A
摘要:
提出一种基于卷积神经网络的自动检测超声图像颈动脉斑块的方法。通过超分辨生成对抗网络提高超声图像质量，并采用高斯混合模型算法结合先验知识自动提取感兴趣区域；最后采用卷积神经网络实现颈动脉有无斑块的自动检测。使用上海市奉贤区中心医院提供的数据集，自动检测颈动脉是否有斑块，模型准确度、敏感度、特异度分别达到94.11%、96.30%、91.67%。实验证明基于卷积神经网络检测颈动脉斑块结果和真实值有很高的一致性，且鲁棒性好。
Abstract:
Abstract: A convolutional neural network-based method is proposed for the automatic detection of carotid plaques in ultrasound images. Super-resolution generative adversarial network is used to improve the quality of ultrasonic image, and Gaussian mixture model algorithm is combined with prior knowledge to automatically extract the region of interest. Finally, the automatic detection of carotid plaques is realized by convolutional neural network. Based on the data set provided by Fengxian District Central Hospital in Shanghai, the proposed method is used to automatically detect whether there is plaque in carotid artery, and finally achieves an accuracy, sensitivity and specificity of 94.11%, 96.30% and 91.67%, respectively. Experiments have proved that the results of carotid plaque detection based on convolutional neural network have a high consistency with the true values, and that the proposed method has a good robustness.

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备注/Memo

备注/Memo:
【收稿日期】2021-07-18 【基金项目】上海市自然科学基金（19ZR1421500）；上海市科委医学引导类科技支撑项目（18411970000）；国家自然科学基金青年基金（81101105） 【作者简介】贺桢，硕士，研究方向：医学图像处理，E-mail: zhenhe1995@163.com 【通信作者】石蕴玉，博士，讲师，主要研究方向：视频智能编码及分析，E-mail: yunyushi@sues.edu.cn；杨少玲，博士后，研究方向：超声诊断及治疗，E-mail: drysl@163.com
更新日期/Last Update: 2022-01-17