Prentašić Pavle, Lončarić Sven
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.
Comput Methods Programs Biomed. 2016 Dec;137:281-292. doi: 10.1016/j.cmpb.2016.09.018. Epub 2016 Oct 6.
Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy.
We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures.
In the validation step using a manually segmented image database we obtain a maximum F measure of 0.78.
As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy.
糖尿病视网膜病变是主要的致残性慢性疾病之一，也是发达国家可预防失明的主要原因之一。糖尿病视网膜病变的早期诊断有助于及时治疗，为此必须投入大量精力开展自动人群筛查项目。在彩色眼底照片中检测渗出物对于糖尿病视网膜病变的早期诊断非常重要。
我们使用深度卷积神经网络进行渗出物检测。为了纳入有关潜在渗出物位置的高级解剖学知识，将卷积神经网络的输出与视盘检测和血管检测程序的输出相结合。
在使用手动分割图像数据库的验证步骤中，我们获得的最大F值为0.78。
由于手动分割和计数渗出物区域是一项繁琐的任务，拥有可靠的自动输出，例如使用卷积神经网络结合其他地标检测器进行自动分割，是创建糖尿病视网膜病变早期检测自动筛查项目的重要一步。
