论文标题
使用深神经网络对光盘和杯子的稳健分割
Robust Segmentation of Optic Disc and Cup from Fundus Images Using Deep Neural Networks
论文作者
论文摘要
视盘（OD）和光杯（OC）是视网膜底面图像中突出临床兴趣的区域。它们是青光眼条件的主要指标。随着深度学习对医疗保健研究的出现和成功，已经提出了几种方法来分割视网膜眼底图像中的重要特征。我们提出了一种新的方法，用于使用基于残留的编码器 - 编码器网络（REDNET）区域卷积神经网络（RCNN）同时分割OD和OC。红色RCNN是由Mask RCNN（MRCNN）激励的。与最先进的技术和对标准公共可用的底面图像数据集的广泛验证的性能比较表明，与MRCNN相比，Red-Rcnn的性能优于性能。 RED-RCNN导致敏感性，特异性，精度，精确度，骰子和JACCARD指数为95.64％，99.9％，99.82％，95.68％，95.64％，91.65％，分别为OD段，91.44％，99.87％，99.83％，85.67％，87.83％，87.83％，87.83％，85.67％，85.67％，87.83.67％，87.83％，87.83％，85.67％，85.67％，85.67％，85.67％，85.67％，85.67％，85.67％，85.67％，85.67％，85.67％。用于OC细分。此外，我们使用基于获得的OD/OC分割计算的杯赛比率（CDR）进行两阶段青光眼严重程度分级。红色RCNN优于MRCNN的优质分割性能转化为青光眼严重程度分级的准确性。
Optic disc (OD) and optic cup (OC) are regions of prominent clinical interest in a retinal fundus image. They are the primary indicators of a glaucomatous condition. With the advent and success of deep learning for healthcare research, several approaches have been proposed for the segmentation of important features in retinal fundus images. We propose a novel approach for the simultaneous segmentation of the OD and OC using a residual encoder-decoder network (REDNet) based regional convolutional neural network (RCNN). The RED-RCNN is motivated by the Mask RCNN (MRCNN). Performance comparisons with the state-of-the-art techniques and extensive validations on standard publicly available fundus image datasets show that RED-RCNN has superior performance compared with MRCNN. RED-RCNN results in Sensitivity, Specificity, Accuracy, Precision, Dice and Jaccard indices of 95.64%, 99.9%, 99.82%, 95.68%, 95.64%, 91.65%, respectively, for OD segmentation, and 91.44%, 99.87%, 99.83%, 85.67%, 87.48%, 78.09%, respectively, for OC segmentation. Further, we perform two-stage glaucoma severity grading using the cup-to-disc ratio (CDR) computed based on the obtained OD/OC segmentation. The superior segmentation performance of RED-RCNN over MRCNN translates to higher accuracy in glaucoma severity grading.
