Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt.
Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA.
Sensors (Basel). 2022 Mar 18;22(6):2342. doi: 10.3390/s22062342.
Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.
糖尿病性视网膜病变（DR）是指糖尿病的眼部并发症。它主要是一种视网膜血管疾病，可导致视力丧失。光学相干断层扫描血管造影（OCTA）显示出检测视网膜血管系统变化的能力，这有助于早期发现 DR。在本文中，我们描述了一种基于捕捉视网膜血管系统的外观和形态标记物从 OCTA 检测 DR 的新框架。这个新框架包括以下主要步骤：（1）基于使用联合马尔可夫-吉布斯随机场（MGRF）模型来模拟 OCTA 图像的外观，从 OCTA 图像中提取视网膜血管系统；（2）估计提取的血管系统内部的距离图，作为描述视网膜血管（RV）系统形态的成像标记物。然后，将 OCTA 图像、提取的血管系统和 RV 估计的距离图组合成一个三维矩阵，作为卷积神经网络（CNN）的输入。使用这种数据表示的主要动机是，它结合了低水平数据和高水平处理数据，使 CNN 能够捕获重要特征，从而提高其区分 DR 与正常视网膜的能力。这已经在多尺度水平上得到了应用，包括原始全维图像以及从原始 OCTA 图像中提取的子图像。该方法在体内数据上进行了测试，使用了约 91 名患者，这些患者由视网膜专家进行了定性分级。此外，还使用基于三个指标的数据集进行了定量验证：灵敏度、特异性和总体准确性。结果表明，该方法的性能优于当前的深度学习和基于特征的 DR 检测方法。
