A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images
A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images
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DOI：
10.1002/mp.12071
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发表时间：
2017-03-01
期刊：
影响因子：
3.8
通讯作者：
El-Azab, Magdi
中科院分区：
文献类型：
--
作者：
ElTanboly, Ahmed;Ismail, Marwa;El-Azab, Magdi
目的：检测方法：提出的计算机辅助诊断（CAD）系统检测DR分三步：（a）在OCT图像上定位和分割12个不同的视网膜层;（B）导出分割的层的特征，以及（c）学习最有区别的特征并将每个对象分类为正常或糖尿病。为了定位和分割视网膜层，OCT图像的信号（强度）使用强度和形状描述符的联合马尔科夫-吉布斯随机场（MGRF）模型来描述。每个分割层的特征在于其局部提取的特征，如反射率，曲率和厚度的累积概率分布函数（CDF）。一个多级深度融合分类网络（DFCN）与一堆非负约束自动编码器（NCAE）被训练来选择最具区分力的视网膜层特征，并使用它们的CDF来检测DR。使用12名正常受试者的OCT扫描和视网膜专家手绘的层图来构建训练图谱。52例临床OCT扫描的初步实验（26名正常人和26名早期DR患者，年龄在40-79岁之间; 40名训练受试者和12名测试受试者）的DR检测准确性、灵敏度和特异性为92%; 83%，100%。100%的准确性，灵敏度和特异性已获得在留一交叉验证测试为所有的52 subjects.Conclusion：无论是定量和视觉评估证实了建议的计算机辅助诊断系统的早期DR检测使用OCT视网膜图像的高准确性。(C)2016年美国医学物理学家协会
Purpose: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances.Methods: The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers' features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts.Results: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40-79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects.Conclusion: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images. (C) 2016 American Association of Physicists in Medicine