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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(5): 985−1004 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(5): 985−1004 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(5): 985−1004 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(5): 985−1004 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: 教育部信息网络工程研究中心资助项目资助
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.E-mail: jwfan@bupt.edu.cn

    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. E-mail: zhangru@bupt.edu.cn

    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. E-mail: buptLumeng@bupt.edu.cn

    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.E-mail: euphy@bupt.edu.cn

    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. E-mail: kangxiaoyang@bupt.edu.cn

    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. E-mail: chaiwenjun@bupt.edu.cn

    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. E-mail: ehaihong@bupt.edu.cn

    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. E-mail: zhonghong.ou@bupt.edu.cn

Applications of Deep Learning Techniques for Diabetic Retinal Diagnosis

Funds: Supported in part by Engineering Research Center of Information Networks of Ministry of Education
More Information
    Author Bio:

    FAN Jia-Wei Master student at Beijing University of Posts and Telecommunications. His research interest covers artificial intelligence and data mining

    ZHANG Ru-Ru Ph.D. candidate at Beijing University of Posts and Telecommunications. Her research interest covers deep learning and medical image processing

    LU Meng Master student at Beijing University of Posts and Telecommunications. Her research interest covers data mining and computer version

    HE Jia-Wen Master student at Beijing University of Posts and Telecommunications. Her research interest covers deep learning and image processing

    KANG Xiao-Yang Master student at Beijing University of Posts and Telecommunications. His research interest covers machine learning and computer version

    CHAI Wen-Jun Master student at Beijing University of Posts and Telecommunications. His research interest covers deep learning and computer vision

    SHI Shen-Da Master student at Beijing University of Posts and Telecommunications. His research interest covers machine learning and computer vision

    SONG Mei-Na Ph.D., professor, Director of the Information Network Engineering Research Center of the Ministry of Education. Her research interest covers service computing, cloud computing, very large scale information service system, and artificial intelligence. Corresponding author of this paper

    E Hai-Hong Ph.D., associate professor, CCSA Mobile Internet Application and Terminal Technology Committee WG1 Deputy Leader. Her research interest covers mobile internet, big data, cloud computing, and artificial intelligence

    OU Zhong-Hong Ph.D., associate professor, and Deputy Dean of the School of Computer Science, Beijing University of Posts and Telecommunications. His research interests cover big data analytics and deep learning techniques

  • 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5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 4 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 dataset of OCT on 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 for OCT 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[51]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[55]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[56]CNN + 手工工程特征 + 随机
    森林 (Random forest) 分类器
    MESSIDOR E-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[53]动态选择抽样策略 (SeS,
    NSeS) + 10 层 CNN
    Kaggle MESSIDORHE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[52]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[59]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[69]诊断 RDRInceptionV3 框架,
    端到端分类
    EyePACS-1
    Messidor-2
    特异性: 93.4 %, 敏感性: 97.5 %
    特异性: 93.9 %, 敏感性: 96.1%
    Li 等[72]诊断有无 DRVggNet, GoogLeNet, Vgg-s 等进行迁移学习DR1
    MESSIDOR
    Vgg-s 性能最好:
    敏感性: 97.11 %, 特异性: 86.03 %,
    准确度: 92.01 %, AUC: 0.9834
    ElTanboly 等[73]诊断 RDRStacked non-negativityconstraint autoencoder (SNCAE)医院获取 OCT 图像准确度: 96 %
    Gargeya 等[74]诊断 RDRData-driven DNN ResNet, Second-level gradientboosting
    classifier
    EyePACS
    MESSIDOR
    AUC: 0.97
    AUC: 0.94
    Abràmoff 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 %,
    特异性: 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, VTDRInceptionV3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 %
    Wang等[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
    Zhou 等[79]划分五类等级Multi-Cell Multi-Task CNNKaggleKappa: 0.841
    Doshi 等[80]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[84]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等[81]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 万幅视网膜 OCT 影像准确性: 93.75%
    Vahadane等[82]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InceptionV3+ 迁移
    学习, 分类网络
    108312 幅 OCT 训练影像 (37206幅脉络膜新生血管, 11349 幅糖尿病黄斑水肿,
    8617 幅玻璃膜疣, 51140 幅正常),
    1000 幅 OCT 测试影像 (每个类别 250 幅)
    准确率: 96.6 %, 敏感性: 97.8 %,
    特异性: 97.4 %, AUC: 99.9 %
    Li 等[83]疾病分类转诊VGG-16 + 迁移学习,
    分类网络
    医院获取 109312 幅 OCT 影像 (37456 幅脉络膜新生血管, 11599 幅糖尿病黄斑水肿, 8867 幅玻璃膜疣, 51390 幅正常)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 幅 OCT 影像准确度: 94%, AUC: 99.21%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-28
  • 录用日期:  2019-06-09
  • 网络出版日期:  2020-12-23
  • 刊出日期:  2021-05-20

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