摘要
目的观察分析基于深度学习的眼底图像视盘定位与分割方法的准确性。方法在ORIGA数据集上训练和评估基于深度学习的视盘定位和分割方法。在深度学习的Caffe框架上构建深度卷积神经网络(CNN)。采用滑动窗口将ORIGA数据集的原图切割成许多小块图片,通过深度CNN判别各个小块图片是否包含完整视盘结构,从而找到视盘所在区域。为避免血管对视盘分割产生影响,在分割视盘边界之前去除视盘区域的血管。采用基于图像像素点分类的视盘分割深度网络,实现眼底图像视盘的分割。计算基于深度学习的眼底图像视盘定位与分割方法的准确性。定位准确率=T/N,T代表视盘定位正确的眼底图像数量,N代表总共用于定位的眼底图像数量。采用重叠误差(overlap error)比较视盘分割结果与实际视盘边界的误差大小。结果基于深度学习的眼底图像视盘定位方法其定位准确率为99.6%;视盘分割平均重叠误差为7.1%;对青光眼图像和正常图像的平均杯盘比的计算误差分别为0.066和0.049;每幅图像的视盘分割平均花费10 ms。结论基于深度学习的眼底图像视盘定位方法能快速并准确地定位视盘区域,同时也能够较为精准地分割出视盘边界。
Objective To observe and analyze the accuracy of the optic disc positioning and segmentation method of fundus images based on deep learning.Methods The model training strategies were training and evaluating deep learning-based optic disc positioning and segmentation methods on the ORIGA dataset.A deep convolutional neural network(CNN)was built on the Caffe framework of deep learning.A sliding window was used to cut the original image of the ORIGA data set into many small pieces of pictures,and the deep CNN was used to determine whether each small piece of picture contained the complete disc structure,so as to find the area of the disc.In order to avoid the influence of blood vessels on the segmentation of the optic disc,the blood vessels in the optic disc area were removed before segmentation of the optic disc boundary.A deep network of optic disc segmentation based on image pixel classification was used to realize the segmentation of the optic disc of fundus images.The accuracy of the optic disc positioning and segmentation method was calculated based on deep learning of fundus images.Positioning accuracy=T/N,T represented the number of fundus images with correct optic disc positioning,and N represented the total number of fundus images used for positioning.The overlap error was used to compare the difference between the segmentation result of the optic disc and the actual boundary of the optic disc.Results On the dataset from ORIGA,the accuracy of the optic disc localization can reach 99.6%,the average overlap error of optic disc segmentation was 7.1%.The calculation errors of the average cup-to-disk ratio for glaucoma images and normal images were 0.066 and 0.049,respectively.Disc segmentation of each image took an average of 10 ms.Conclusion The algorithm can locate the disc area quickly and accurately,and can also segment the disc boundary more accurately.
作者
万程
周雪婷
周鹏
沈建新
俞秋丽
Wan Cheng;Zhou Xueting;Zhou Peng;Shen Jianxin;Yu Qiuli(College of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Department of Ophthalmology,The Second Affiliated Hospital of Nanjing Medical University,Nanjing 210003,China)
出处
《中华眼底病杂志》
CAS
CSCD
北大核心
2020年第8期628-632,共5页
Chinese Journal of Ocular Fundus Diseases
基金
中国博士后科学基金(2019M661832)
江苏省博士后科研资助计划(2019K226)
江苏高校优势学科建设工程项目。
关键词
神经网络(计算机)
深度学习
视盘定位
视盘分割
Neural networks(computer)
Deep learning
Optic disk localization
Optic disk segment
