Automatic detection of papilledema through fundus retinal images using deep learning
Automatic detection of papilledema through fundus retinal images using deep learning
复制标题
DOI：
10.1002/jemt.23865
复制
发表时间：
2021-07-08
影响因子：
2.5
通讯作者：
Ali Bahaj, Saeed
中科院分区：
文献类型：
--
作者：
Saba, Tanzila;Akbar, Shahzad;Ali Bahaj, Saeed
视乳头水肿是一种视网膜综合征，视网膜视神经因颅内压升高而扩张。视乳头水肿的异常，如视网膜神经纤维层(RNFL)混浊，可导致失明。这些异常可以通过眼底相机捕捉到的视网膜图像来看到。本文提出了一种基于深度学习的乳头肿自动检测系统，该系统通过U网和稠密网两种结构对乳头肿进行检测和分级。建议的方法有两个主要阶段。首先，对眼底视网膜图像中的视盘及其周围区域进行定位和裁剪，输入致密网络，将视盘分为视乳头水肿或正常视盘。其次，利用Gabor滤波对密集网络分类的乳头状水肿眼底图像进行了预处理。将处理后的乳头状水肿图像输入U-Net，得到分割后的血管网络，计算血管不连续指数(VDI)和血管临近程度不连续指数(VDIP)，对乳头状水肿进行分级。VDI和VDIP是检查视乳头水肿严重程度和分级的标准参数。用STARE数据集采集的60例乳头水肿和40例正常眼底图像对该系统进行了评估。实验结果表明，密网对乳头水肿分类的灵敏度为98.63%，特异度为97.83%，准确率为99.17%。同样，U-net对轻度和重度乳头水肿的分级结果也要好得多，敏感性为99.82%，特异性为98.65%，准确性为99.89%。基于深度学习的乳头水肿自动检测和分级用于临床，是目前最先进的研究成果。
Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.