Wang Zhuo, Camino Acner, Zhang Miao, Wang Jie, Hwang Thomas S, Wilson David J, Huang David, Li Dengwang, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR, 97239, USA.
Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China.
Biomed Opt Express. 2017 Nov 7;8(12):5384-5398. doi: 10.1364/BOE.8.005384. eCollection 2017 Dec 1.
Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.
糖尿病性视网膜病变是一种病理状况，其中微血管循环异常最终导致光感受器破坏，进而导致永久性视力丧失。在此，我们开发了一种方法，通过绘制光学相干断层扫描图像中的椭圆体区反射率异常来自动检测轻度糖尿病性视网膜病变中的光感受器破坏。该算法使用具有重新定义的隶属函数的模糊 c 均值方案，为每个像素分配缺陷严重程度级别，并生成缺陷类别归属的概率图。一种新颖的无监督聚类优化方案可准确检测受影响区域。在 13 名患病受试者群体上，所实现的准确率、灵敏度和特异性约为 90%。该方法显示出在糖尿病性视网膜病变进展中准确快速检测早期生物标志物的潜力。
