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深度学习方法在糖尿病视网膜病变诊断中的应用

范家伟 张如如 陆萌 何佳雯 康霄阳 柴文俊 石珅达 宋美娜 鄂海红 欧中洪

范家伟, 张如如, 陆萌, 何佳雯, 康霄阳, 柴文俊, 石珅达, 宋美娜, 鄂海红, 欧中洪. 深度学习方法在糖尿病视网膜病变诊断中的应用. 自动化学报, 2021, 47(3): 1−20 doi: 10.16383/j.aas.c190069
引用本文: 范家伟, 张如如, 陆萌, 何佳雯, 康霄阳, 柴文俊, 石珅达, 宋美娜, 鄂海红, 欧中洪. 深度学习方法在糖尿病视网膜病变诊断中的应用. 自动化学报, 2021, 47(3): 1−20 doi: 10.16383/j.aas.c190069
Fan Jia-Wei, Zhang Ru-Ru, Lu Meng, He Jia-Wen, Kang Xiao-Yang, Chai Wen-Jun, Shi Shen-Da, Song Mei-Na, E Hai-Hong, Ou Zhong-Hong. Applications of deep learning techniques for diabetic retinal diagnosis. Acta Automatica Sinica, 2021, 47(3): 1−20 doi: 10.16383/j.aas.c190069
Citation: Fan Jia-Wei, Zhang Ru-Ru, Lu Meng, He Jia-Wen, Kang Xiao-Yang, Chai Wen-Jun, Shi Shen-Da, Song Mei-Na, E Hai-Hong, Ou Zhong-Hong. Applications of deep learning techniques for diabetic retinal diagnosis. Acta Automatica Sinica, 2021, 47(3): 1−20 doi: 10.16383/j.aas.c190069

深度学习方法在糖尿病视网膜病变诊断中的应用

doi: 10.16383/j.aas.c190069
基金项目: 国家重点研发计划(2017YFB1400802)资助
详细信息
    作者简介:

    范家伟:北京邮电大学硕士研究生. 主要研究方向为人工智能与数据挖掘

    张如如:北京邮电大学博士研究生. 主要研究方向为深度学习和医学影像处理

    陆萌:北京邮电大学硕士研究生. 主要研究方向为数据挖掘和计算机视觉

    何佳雯:北京邮电大学硕士研究生. 主要研究方向为深度学习和图像处理

    康霄阳:北京邮电大学硕士研究生. 主要研究方向为机器学习和计算机视觉

    柴文俊:北京邮电大学硕士研究生. 主要研究方向为深度学习和计算机视觉

    石珅达:北京邮电大学硕士研究生. 主要研究方向为机器学习和计算机视觉. E-mail: cy.z.feng@gmail.com

    宋美娜:教授, 教育部信息网络工程研究中心主任. 主要研究方向为服务计算, 云计算, 超大规模信息服务系统和人工智能. 本文通信作者. E-mail: mnsong@gmail.com

    鄂海红:副教授, CCSA移动互联网应用与终端技术委员会WG1副组长. 主要研究方向为移动互联网、大数据、云计算和人工智能

    欧中洪:副教授, 北京邮电大学计算机学院副院长. 主要研究方向为大数据分析、深度学习技术

Applications of Deep Learning Techniques for Diabetic Retinal Diagnosis

Funds: Supported in part by National Key Research and Development Program of China (2017YFB1400802)
  • 摘要: 深度学习可以有效提取图像隐含特征,在医学影像识别方面的应用快速发展. 由于糖尿病视网膜病变(Diabetic retinopathy, DR)诊断标准明确、分类体系成熟,应用深度学习诊断糖尿病视网膜病变近年来成为研究热点. 本文从深度学习方法在DR诊断中的最新研究进展、DR诊断的一般流程、公共数据集、医学影像标注方法、主要实现模型、面临的主要挑战几方面, 对深度学习方法在糖尿病视网膜病变诊断中的应用进行了详细综述, 便于更多机器视觉、尤其是深度学习医学影像的研究者们参照对比,加快该领域研究的成熟度和临床落地应用.
  • 图  1  基于深度学习DR诊断的一般框架

    Fig.  1  General framework of diabetic retinal diagnosis based on deep learning

    图  2  糖尿病视网膜病灶区域检测

    Fig.  2  Regional detection of diabetic retinopathy

    图  3  糖尿病视网膜病变等级分类

    Fig.  3  Classification of diabetic retinopathy

    表  1  糖尿病视网膜病变国际分级标准

    Table  1  International classification of diabetic retinopathy and diabetic macular edema

    糖尿病视网膜病变散瞳后眼镜所见随访建议
    无视网膜病变(无DR)无异常1 ~ 2 年随访一次
    轻度非增殖性糖尿病视网膜
    病变(轻度NPDR)
    仅有微动脉瘤1 ~ 2 年随访一次
    中度非增殖性糖尿病视网膜
    病变(中度NPDR)
    比仅有微动脉瘤重, 比重者轻半年到一年随访一次或转
    诊至眼科医师
    重度非增殖性糖尿病视网
    膜病变(重度NPDR)
    有以下任一症状之一: 4个象限每个都有20个以上的内出血病灶;
    2个以上象限有确定的静脉珠状改变; 1个以上象限有明显的视网膜
    内微血管异常并且无增殖性病变体征
    转诊至眼科医师
    增殖性糖尿病视网膜
    病变(PDR)
    具有重度非增殖性症状且有以下一种或多种情形:
    新生血管, 玻璃体积血/视网膜前出血
    转诊至眼科医师
    下载: 导出CSV

    表  2  糖尿病视网膜病变公共数据集

    Table  2  Public data set on diabetic retinal

    名称数据类型图像信息图片尺寸图片格式数据量 (幅)获取权限链接
    IDRID[24]CFP图像按照国际标准进行糖网和黄斑水肿分级, 并对其中81幅有糖网征象的图像进行了病变的像素级标注.4228 × 2848JPEG516注册IEEE账
    号获取
    https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid
    E-Ophtha[25]CFP由e-ophtha-MA (微动脉瘤)和e-ophtha-EX (渗出)两个子数据库组成. 标注了EX和MA的区域, 以掩模的方式给出.2544 × 1696
    1440 × 960
    1504 × 1000等
    JPEG463提交邮箱以获
    取下载码
    https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid
    DRiDB[26]CFP每幅图像由至少五名专家标注, 标注内容包含所有视网膜主要解剖结构和病理特征以及糖网分级.720 × 676BMP50发送邮件来请求
    访问数据库
    https://ipg.fer.hr/ipg/resources/image_database
    Messidor[27]CFP图像标注了糖尿病性视网膜病变分级及黄斑水肿分级.1400 × 960
    2 240 × 1488
    2304 × 1536
    TIFF1200提交邮箱以获
    取下载码
    http://www.adcis.net/en/Download-Third-Party/Messidor.html
    Messidor-2[28]CFP每幅图像标注了糖尿病性视网膜病变分级及黄斑水肿分级.1400 × 960
    2240 × 1488
    2304 × 1536
    TIFF1784提交邮箱以获
    取下载码
    http://latim.univ-brest.fr/indexfce0.html
    DIARETDB0[29]CFP该数据集是用于糖尿病视网膜病变检测的基准的公开数据集, 每幅图像标注了病变信息.1500 × 1152PNG130直接下载http://www.it.lut.fi/project/imageret/diaretdb0/index.html
    DIARETDB1[30]CFP该数据集可用做基准糖尿病视网膜病变检测数据集, 由四名医学专家对糖网相关病变区域进行
    标注.
    1500 × 1152PNG89直接下载http://www.it.lut.fi/project/imageret/diaretdb1/
    Large datasetof OCTon Mendeley[15]OCT图像分为训练集和测试集, 每个集合含有4种标签的数据: CNV, DME, DR-USEN和NORMAL.不统一约为
    400 × 500
    JPEG>50000直接下载https://data.mendeley.com/archiver/rscbjbr9sj?version=3
    Dataset forOCT Classification[31]OCT由50个正常, 48个干性AMD和50个DME组成.765 × 765TIFF3700直接下载https://sites.google.com/site/hosseinrabbanikhorasgani/datasets-1
    下载: 导出CSV

    表  3  病变区域检测相关研究

    Table  3  Related works on lesion detection

    相关研究方法数据集提取特征性能
    Shan等[52]Patches + 堆叠稀疏自动编码(SSAE) + 迁移学习DIARETDBMA敏感性: 91.6%, F-score: 91.3%, 准确性: 91.38%
    Budak等[53]深度卷积神经网络(DCNN)在线挑战数据集 (ROC)MA比赛分数为0.221, 高于其他方法
    Dai等[54]Alex-Net框架为基础的MS-CNN模型当地医院收集数据, DIARETDB1MA准确率: 96.1%
    Orlando等[55]CNN+手工工程特征+随机森林(Random forest)分类器MESSIDORe-ophthaMA, HEAUC: CNN: 0.7912 手工工程: 0.7325 CNN+手工工程: 0.8932 AUC: CNN: 0.8374 手工工程: 0.8812 CNN+手工工程: 0.9031
    van Grinsven等[56]动态选择抽样策略(SeS、NSeS)+10层CNNKaggleMESSIDORHE敏感性: 84.8% 特异性: 90.4% AUC: 91.7% 敏感性: 93.1% 特异性: 91.5% AUC: 97.9%
    Prentasic等[57]DNNDRiDBEX, SE敏感性: 78% F-score: 78%
    Otálora等[58]基于LeNet网络+EGL的主动学习策略+迁移学习E-OphthaEX, SE敏感性: 99.8% 特异性: 99.6% 准确性: 99.6%
    Abbasi-Sureshjani等[60]ResNetDIARETDB1, DR2, E-OphthaEX, SEAUC分别为96.5%, 97.2 %, 99.4%
    Badar等[40]SegNet模型为基础的Auto-Encoder网络MessidorMA, HE, EX精确度: 99.24% (EX),
    97.86% (HM), 88.65% (MA)
    ISBI韩国VRT团队DeepLab+U-NetDRiDBMA, HE, SE, EXF-score分别为: 0.4951, 0.6804, 0.6995, 0.7127
    ISBI中国平安科技Patech团队DenseNet+DeepLab V3DRiDBMA, HE, EXF-score分别为: 0.474, 0.649, 0.885
    ISBI中国科大讯飞U-Net+注意力机制+DeepLabV3+DRiDBMA, HE, SE, EXF-score分别为: 0.5017, 0.5588, 0.6588, 0.8741
    Tan等[63]DCNNCLEOPATRAMA, HM, HE, SE敏感性: 87.58% 特异性: 98.73%
    下载: 导出CSV

    表  4  病变等级分类相关研究

    Table  4  Related works on classification of diabetic retinopathy

    相关研究应用方法数据集性能
    谷歌Gulshan等[70]诊断RDRInception-v3框架,
    端到端分类
    EyePACS-1Messidor-2特异性: 93.4 %, 敏感性: 97.5 %; 特异性: 93.9 %, 敏感性: 96.1%;
    Xiaogang Li等[72]诊断有无DRVggNet, GoogLeNet, Vgg-s等进行迁移学习DR1MESSIDORVgg-s性能最好: 敏感性:
    97.11 %, 特异性: 86.03 %, 准确度: 92.01 %, AUC: 0.9834
    Ahmed ElTanboly等[73]诊断RDRStacked non-negativityconstraint autoencoder(SNCAE)医院获取OCT图像准确度: 96 %
    Rishab Gargeya等[74]诊断RDRData-driven DNN ResNet, Second-level gradientboosting
    classifier
    EyePACSMESSIDORAUC: 0.97; AUC: 0.94;
    Abramoff等[75]诊断RDR, VTDRDCNNMessidor-2RDR敏感性: 96.8 %, 特异性: 87.0 %, AUC: 0.980. 从RDR分类出VTDR: 敏感性: 100 %, 异性: 91 %, AUC: 0.989.
    Ting等[76]诊断RDR, VTDRVGG-196个不同国家招募了10组
    数据集
    RDR敏感性: 90.5 %, 特异性: 91.6 %, AUC: 0.936. 从RDR分类出VTDR: 敏感性: 100 %, 特异性: 91.1 %, AUC: 0.958.
    中山大学[16]诊断RDR, VTDRInception-v3中国人彩色眼底图片多种
    族彩色眼底图像
    敏感性: 97.0 %, 特异性: 91.4 %; 敏感性: 92.5 %, 特异性: 98.5 %;
    Abràmoff等[77]在初级保健诊所诊断
    DR, 进行实际应用
    CNN在初级保健诊所招收了900
    名受试者, 男性占47.5 %; 其
    中包括: 西班牙裔16.1 %, 非
    裔美国人28.6%.
    敏感性: 87.2 %, 特异性: 90.7 %, 显像率: 96.1 %.
    ISBI Mammoth团队[78]划分五类等级DenseNet, Boosting
    tree algorithm
    Kaggle精确度: R0: 0.92, R1: 0.70, R2: 0.64, R3: 0.67, R4: 0.69.
    Kang Zhou等[83]划分五类等级Multi-Cell Multi-Task CNNKaggleKappa: 0.841
    Darshit Doshi等[84]划分五类等级5层CNN网络EyePACsKappa: 0.386
    IBM划分五类等级DCNNEyePAC准确度: 86 %
    下载: 导出CSV

    表  5  基于视网膜OCT影像的眼部疾病诊断相关研究

    Table  5  Studies on diagnosis of ocular diseases based on retinal OCT images

    相关研究应用方法数据集性能
    Sandhu等[62]对DR患者早期诊断基于融合形状, 强度和空间信息的联合模型, 两阶段深度融合分类网络路易斯维尔大学(University of Louisville) 接受常规筛查和(或)监测检查的II型糖尿病患者 OCT 数据准确率平均为94%
    Hassan[80]从OCT扫描中分割出
    8个视网膜层
    深度卷积神经网络, 基于结构张力的分割框架(CNN-STSF)取自不同公共可用数据集和当地武装部队眼科研究所(AFIO)数据集的超过3.9万张视网膜 OCT影像准确性: 93.75%
    Vahadane[81]分割硬渗出物和囊肿
    区域检测DME
    图像处理, 深度学习,
    基于规则方法
    1827幅 OCT影像Precision: 96.43%, Recall:
    89.45%, F1-score: 92.81%
    Kermany等[15]疾病分类转诊Inception-v3+迁移
    学习, 分类网络
    108312张OCT训练影像(37206张脉络膜新生血管, 11349张糖尿病黄斑水肿,
    8617张玻璃膜疣, 51 140张正常),
    1000张OCT测试影像(每个类别250张)
    准确率: 96.6 %, 敏感性: 97.8 %,
    特异性: 97.4 %, AUC: 99.9 %
    Li等[79]疾病分类转诊VGG-16+迁移学习,
    分类网络
    医院获取109312张OCT影像(37456张脉络膜新生血管, 11599张糖尿病黄斑水肿, 8867 张玻璃膜疣, 51390正常)准确率: 98.6%, 敏感性: 97.8%,
    特异性: 99.4%, AUC: 100%
    DeepMind
    团队[18]
    分割疾病特征分类转诊3DU-NET分割网络, CNN分类网络摩尔菲尔兹眼科医院提供14 884张OCT影像准确度: 94%, AUC: 99.21%
    下载: 导出CSV
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  • 收稿日期:  2019-01-28
  • 录用日期:  2019-06-09
  • 网络出版日期:  2020-12-23

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