基于改进U-Net的视盘视杯分割方法的研究

基于数字眼底图像进行视盘视杯分割是青光眼常用的诊断方法。为了更加精确地分割视盘视杯,提出了一种基于改进U-Net的视盘视杯分割方法。在传统U-Net的基础上,使用残差块改进了下采样部分,并使用卷积操作改进U-net中的跳层连接部分,使网络更加充分地获取特征信息。使用多种性能指标对训练的模型进行评价,结果表明,视盘模型和视杯模型在DRISHTI-GS数据集上的DICE系数分别达到了98.3%和97.2%,IOU系数分别达到了93.2%和88.5%。

Research on the segmentation of optic disc and cup based on modified U-Net

In the diagnosis of glaucoma, segmentation of optic cup and optic disc based on digital fundus image is a common diagnostic method. In order to segment the cup and disc accurately, we proposed a segmentation method based on the improved U-Net. Compared with the traditional U-Net, a residual block was used to improve the down sampling part, and convolution part was used to improve the skip connection, so that the network could obtain more sufficient feature information. The Dice and IOU of the optic disc segmentation model and the optic cup segmentation model on DRISHTI-GS data set reached 98.3% and 97.2%, 93.2% and 88.5%.