基于深度卷积神经网络的糖尿病视网膜病变分期及病灶检测

doi:10.11772/j.issn.1001-9081.2019122198

计算机应用 ›› 2020, Vol. 40 ›› Issue (8): 2460-2464.DOI: 10.11772/j.issn.1001-9081.2019122198

• 应用前沿、交叉与综合 • 上一篇下一篇

基于深度卷积神经网络的糖尿病视网膜病变分期及病灶检测

谢云霞¹, 黄海于¹, 胡建斌²

1. 西南交通大学信息科学与技术学院, 成都 611756;
2. 爱尔眼科成都东区爱尔眼科医院, 成都 610056

收稿日期:2019-12-31 修回日期:2020-03-05 出版日期:2020-08-10 发布日期:2020-05-13
通讯作者: 胡建斌(1964-),男,重庆人,主任医师,硕士,主要研究方向:玻璃体视网膜临床与基础,449703499@qq.com
作者简介:谢云霞(1994-),女,四川成都人,硕士研究生,主要研究方向:医学图像处理;黄海于(1970-),男,重庆人,副教授,硕士,主要研究方向:医学图像处理。

Staging and lesion detection of diabetic retinopathy based on deep convolution neural network

XIE Yunxia¹, HUANG Haiyu¹, HU Jianbin²

1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 611756, China;
2. Chengdu East Aier Eye Hospital, Aier Eye Hospital, Chengdu Sichuan 610056, China

Received:2019-12-31 Revised:2020-03-05 Online:2020-08-10 Published:2020-05-13

摘要/Abstract

摘要： 针对糖尿病视网膜病变（DR）图像分辨率过大、病灶特征过于分散难以获取以及正负难易样本不平衡而导致DR分期精确率一直无法得到有效提高的问题，提出了改进的基于快速区域的卷积神经网络（Faster R-CNN）和子图分割相结合的DR分期方法。首先，使用子图分割解决视盘区域对于病灶识别的干扰问题；其次，在特征提取阶段使用深度残差网络以解决病灶在高分辨率眼底图像中占比小而导致的特征难以获取的问题；最后，在感兴趣区域（ROI）生成时采用在线困难样本挖掘（OHEM）方法解决正负难易样本不平衡的问题。在国际公开数据集EyePACS进行DR分期实验，所提方法在DR病分期中精确率0期达到94.83%，1期达到86.84%，2期达到94.00%，3期达到87.21%，4期达到82.96%。实验结果表明，改进后的Faster R-CNN能对DR图像高效分期并自动标注出病灶。

关键词: 糖尿病视网膜病变, 目标检测, 基于快速区域的卷积神经网络算法, 子图分割, 在线困难样本挖掘

Abstract: For Diabetic Retinopathy (DR), the image resolution is too high, the lesion features are too scattered to obtain, and the positive, negative, hard and easy samples are imbalanced, thus the DR staging accuracy cannot be effectively improved. Therefore, a DR staging method based on the combination of improved Faster Region-based Convolutional Neural Network (Faster R-CNN) and subgraph segmentation was proposed. First, subgraph segmentation was used to solve the interference problem of the optic disc region to lesion recognition. Second, a deep residual network was used in the feature extraction process to solve the problem of difficulty of obtaining features due to the small proportion of the lesions in the high-resolution fundus image. Finally, the Online Hard Example Mining (OHEM) method was used to solve the problem of imbalance between positive, negative, hard and easy samples during the generation of Region of Interest (ROI). In the DR staging experiments on EyePACS, an internationally open dataset, the accuracy of the proposed method in DR staging reached 94.83% in stage 0, 86.84% in stage 1, 94.00% in stage 2, 87.21% in stage 3 and 82.96% in phase 4. Experimental results show that the improved Faster R-CNN can efficiently stage DR images and automatically label the lesions.

Key words: Diabetic Retinopathy (DR), object detection, Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm, subgraph segmentation, Online Hard Example Mining (OHEM)

中图分类号:

TP391.41

谢云霞, 黄海于, 胡建斌. 基于深度卷积神经网络的糖尿病视网膜病变分期及病灶检测[J]. 计算机应用, 2020, 40(8): 2460-2464.

XIE Yunxia, HUANG Haiyu, HU Jianbin. Staging and lesion detection of diabetic retinopathy based on deep convolution neural network[J]. Journal of Computer Applications, 2020, 40(8): 2460-2464.

参考文献

[1] ROY T, DEBORAHB. 糖尿病视网膜病变筛查手册诊断和处理[M].2版.陈浩宇,译.北京:人民卫生出版社, 2016:3. (ROY T, DEBORAH B. Handbook of Retinal Screening in Diabetes:Diagnosis and Management[M]. 2nd ed. CHEN H Y, translated. Beijing:People's Medical Publishing House, 2016:3.)
[2] 陈喆,张士胜,朱惠敏,等.糖尿病视网膜病变的国际临床分类分析[J].国际眼科杂志,2011,11(8):1394-1401. (CHEN Z, ZHANG S S, ZHU H M, et al. Analysis of international clinical diabetic retinopathy disease severity scale[J]. International Journal of Ophthalmology, 2011, 11(8):1394-1401.)
[3] WANG Z, YIN Y, SHI J, et al. Zoom-in-Net:deep mining lesions for diabetic retinopathy detection[C]//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS 10435. Cham:Springer, 2017:267-275.
[4] CHETOUI M, AKHLOUFI M A, KARDOUCHI M. Diabetic retinopathy detection using machine learning and texture features[C]//Proceedings of the 2018 IEEE Canadian Conference on Electrical and Computer Engineering. Piscataway:IEEE, 2018:1-4.
[5] HARUN N H, YUSOF Y, HASSAN F, et al. Classification of fundus images for diabetic retinopathy using artificial neural network[C]//Proceedings of the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology. Piscataway:IEEE, 2019:498-501.
[6] GARGEYA R, LENG T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 2017, 124(7):962-969.
[7] PRATT H, COENEN F, BROADBENT D M, et al. Convolutional neural networks for diabetic retinopathy[J]. Procedia Computer Science, 2016, 90:200-205.
[8] GULSHAN V, PENG L, CORAM M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA:The Journal of American Medical Association, 2016, 316(22):2402-2410.
[9] 彭英辉,张东波,沈奔. 基于多尺度匹配滤波和集成学习的眼底图像微脉瘤检测[J]. 计算机应用, 2013, 33(2):543-546, 566. (PENG Y H, ZHANG D B, SHEN B. Microaneurysm detection based on multi-scale match filtering and ensemble learning[J]. Journal of Computer Applications, 2013, 33(2):543-546, 566.)
[10] SEOUD L, HURTUT T, CHELBI J, et al. Red lesion detection using dynamic shape features for diabetic retinopathy screening[J]. IEEE Transactions on Medical Imaging, 2016, 35(4):1116-1126.
[11] JAAFAR H F, NANDI A K, AL-NUAIMY W. Automated detection and grading of hard exudates from retinal fundus images[C]//Proceedings of the 19th European Signal Processing Conference. Piscataway:IEEE, 2011:66-70.
[12] HALOI M, DANDAPAT S, SINHA R. Gaussian scale space approach for exudates detection, classification and severity prediction[EB/OL].[2019-11-02].https://arxiv.org/pdf/1505.00737.pdf.
[13] REN S, HE K, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[14] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778
[15] SHRIVASTAVA A, GUPTA A, GIRSHICK R. Training region-based object detectors with online hard example mining[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:761-769.
[16] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:2818-2826.
[17] ZENG X, CHEN H, LUO Y, et al. Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network[J]. IEEE Access, 2019, 7(1):30744-30753.

基于深度卷积神经网络的糖尿病视网膜病变分期及病灶检测

Staging and lesion detection of diabetic retinopathy based on deep convolution neural network

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	徐江浪, 李林燕, 万新军, 胡伏原. 结合目标检测的室内场景识别方法[J]. 计算机应用, 2021, 41(9): 2720-2725.
[2]	马佳良, 陈斌, 孙晓飞. 基于改进的Faster R-CNN的通用目标检测框架[J]. 计算机应用, 2021, 41(9): 2712-2719.
[3]	陈静, 毛莺池, 陈豪, 王龙宝, 王子成. 基于改进单点多盒检测器的大坝缺陷目标检测方法[J]. 计算机应用, 2021, 41(8): 2366-2372.
[4]	樊玮, 李晨炫, 邢艳, 黄睿, 彭洪健. 航空发动机损伤图像的二分类到多分类递进式检测网络[J]. 计算机应用, 2021, 41(8): 2352-2357.
[5]	肖振远, 王逸涵, 罗建桥, 熊鹰, 李柏林. 基于部分加权损失函数的RefineDet[J]. 计算机应用, 2021, 41(7): 1928-1932.
[6]	李朝, 兰海, 魏宪. 基于注意力的毫米波-激光雷达融合目标检测[J]. 计算机应用, 2021, 41(7): 2137-2144.
[7]	高钦泉, 黄炳城, 刘文哲, 童同. 基于改进CenterNet的竹条表面缺陷检测方法[J]. 计算机应用, 2021, 41(7): 1933-1938.
[8]	侯笑晗, 金国栋, 谭力宁, 薛远亮. 基于自适应和最优特征的合成孔径雷达舰船检测方法[J]. 计算机应用, 2021, 41(7): 2150-2155.
[9]	后云龙, 朱磊, 陈琴, 吕燧栋. 基于高斯差分特征网络的显著目标检测[J]. 计算机应用, 2021, 41(3): 706-713.
[10]	曹建荣, 吕俊杰, 武欣莹, 张旭, 杨红娟. 融合运动特征和深度学习的跌倒检测算法[J]. 计算机应用, 2021, 41(2): 583-589.
[11]	蒋宁, 方景龙, 杨庆. 基于单点多盒检测器的全局-局部层级的域适应目标检测[J]. 计算机应用, 2021, 41(2): 517-522.
[12]	汪虹余, 张彧, 杨恒, 穆楠. 基于蚁群优化算法的弱光图像显著性目标检测[J]. 计算机应用, 2021, 41(10): 2970-2978.
[13]	裴仪瑶, 郭会明, 张丹普, 陈文博. 基于定位不确定性的鲁棒3D目标检测方法[J]. 计算机应用, 2021, 41(10): 2979-2984.
[14]	郑思诚, 孔令华, 游通飞, 易定容. 动态环境下基于深度学习的语义SLAM算法[J]. 计算机应用, 2021, 41(10): 2945-2951.
[15]	方书雅, 刘守印. 基于学生人体检测的无感知课堂考勤方法[J]. 计算机应用, 2020, 40(9): 2519-2524.