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

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

范家伟¹,
张如如^{1, 2},
陆萌^{1, 2},
何佳雯^{1, 2},
康霄阳^{1, 2},
柴文俊^{1, 2},
石珅达^{1, 2,},
宋美娜^{1, 2,},
鄂海红^{1, 2},
欧中洪^{1, 2}

1.
北京邮电大学计算机学院北京 100089
2.
教育部信息网络工程研究中心(北京邮电大学) 北京 100089

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

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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

Applications of Deep Learning Techniques for Diabetic Retinal Diagnosis

FAN Jia-Wei¹,
ZHANG Ru-Ru^{1, 2},
LU Meng^{1, 2},
HE Jia-Wen^{1, 2},
KANG Xiao-Yang^{1, 2},
CHAI Wen-Jun^{1, 2},
SHI Shen-Da^{1, 2
,},
SONG Mei-Na^{1, 2
,},
E Hai-Hong^{1, 2},
OU Zhong-Hong^{1, 2}

1.
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100089
2.
Information Network Engineering Research Center, Ministry of Education (Beijing University of Posts and Telecommunications), Beijing 100089

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

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.
- 深度学习 /
- 糖尿病 /
- 糖尿病视网膜病变 /
- 智能诊断 /
- 图像标注 /
- 病变区域检测 /
- 病变等级分类
Abstract:
Deep learning can effectively extract the hidden features of image and its application in medical image recognition is developing rapidly. Due to the clear diagnostic criteria for diabetic retinopathy (DR) and the mature classification system, the application of deep learning to diagnose diabetic retinopathy has become a research hotspot in recent years. Therefore, this paper reviews the application of deep learning methods in the diagnosis of diabetic retinopathy detailedly based on the latest research progress of deep learning for DR diagnosis, the general flow for DR diagnosis, public dataset, medical image annotation method, main models and major challenges. It brings convenience for more researchers of computer vision deepling learning, especially medical imaging deep learning, to speed up the research maturity and clinical application in this field.
- Deep learning /
- diabetes mellitus (DM) /
- diabetic retinopathy (DR) /
- intelligent diagnosis /
- image annotation /
- lesions detection /
- lesions classification

HTML全文

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

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图 2 糖尿病视网膜病灶区域检测

Fig. 2 Regional detection of diabetic retinopathy

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图 3 糖尿病视网膜病变等级分类

Fig. 3 Classification of diabetic retinopathy

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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

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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 × 2848	JPEG	516	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	JPEG	463	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 × 676	BMP	50	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	TIFF	1200	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	TIFF	1784	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 × 1152	PNG	130	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 × 1152	PNG	89	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 × 765	TIFF	3700	直接下载	https://sites.google.com/site/hosseinrabbanikhorasgani/datasets-1

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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	DIARETDB	MA	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, DIARETDB1	MA	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-Ophtha	MA, HE	AUC: 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 MESSIDOR	HE	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]	DNN	DRiDB	EX, 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-Ophtha	EX, SE	敏感性: 99.8%, 特异性: 99.6%, 准确性: 99.6%
Abbasi-Sureshjani 等^[52]	ResNet	DIARETDB1, DR2, E-Ophtha	EX, SE	AUC 分别为: 96.5%, 97.2 %, 99.4%
Badar 等^[40]	SegNet 模型为基础的 Auto-encoder 网络	Messidor	MA, HE, EX	精确度: 99.24% (EX), 97.86% (HM), 88.65% (MA)
ISBI 韩国 VRT 团队	DeepLab + U-Net	DRiDB	MA, HE, SE, EX	F-score 分别为: 0.4951, 0.6804, 0.6995, 0.7127
ISBI 中国平安科技 Patech 团队	DenseNet + DeepLab V3	DRiDB	MA, HE, EX	F-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 +	DRiDB	MA, HE, SE, EX	F-score 分别为: 0.5017, 0.5588, 0.6588, 0.8741
Tan 等^[59]	DCNN	CLEOPATRA	MA, HM, HE, SE	敏感性: 87.58%, 特异性: 98.73%

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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 RDR	InceptionV3 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 DR	VggNet, 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 RDR	Stacked non-negativityconstraint autoencoder (SNCAE)	医院获取 OCT 图像	准确度: 96 %
Gargeya 等^[74]	诊断 RDR	Data-driven DNN ResNet, Second-level gradientboosting classifier	EyePACS MESSIDOR	AUC: 0.97 AUC: 0.94
Abràmoff 等^[75]	诊断 RDR, VTDR	DCNN	Messidor-2	RDR 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, VTDR	VGG-19	6 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, VTDR	InceptionV3	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 CNN	Kaggle	Kappa: 0.841
Doshi 等^[80]	划分五类等级	5层 CNN 网络	EyePACs	Kappa: 0.386
IBM	划分五类等级	DCNN	EyePAC	准确度: 86 %

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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%

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