Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment

被引:146
作者
Diaz-Pinto, Andres [1 ]
Colomer, Adrian [1 ]
Naranjo, Valery [1 ]
Morales, Sandra [1 ]
Xu, Yanwu [2 ]
Frangi, Alejandro F. [3 ]
机构
[1] Univ Politecn Valencia, Inst Invest & Innovac Bioingn, E-46022 Valencia, Spain
[2] Baidu Inc, Artificial Intelligence Innovat Business, Beijing 100193, Peoples R China
[3] Univ Leeds, CISTIB Ctr Computat Imaging & Simulat Technol Bio, Leeds LS2 9JT, W Yorkshire, England
基金
欧盟地平线“2020”; 英国工程与自然科学研究理事会;
关键词
Glaucoma assessment; retinal image synthesis; fundus images; DCGAN; medical imaging; OPTIC-NERVE HEAD; COLOR IMAGES;
D O I
10.1109/TMI.2019.2903434
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifier. In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial model on a small glaucoma-labeled database and a large unlabeled database. Various studies have shown that glaucoma can be monitored by analyzing the optic disc and its surroundings, and for that reason, the images used in this paper were automatically cropped around the optic disc. The novelty of this paper is to propose a new retinal image synthesizer and a semi-supervised learning method for glaucoma assessment based on the deep convolutional GANs. In addition, and to the best of our knowledge, this system is trained on an unprecedented number of publicly available images (86 926 images). This system, hence, is not only able to generate images synthetically but to provide labels automatically. Synthetic images were qualitatively evaluated using t-SNE plots of features associated with the images and their anatomical consistency was estimated by measuring the proportion of pixels corresponding to the anatomical structures around the optic disc. The resulting image synthesizer is able to generate realistic (cropped) retinal images, and subsequently, the glaucoma classifier is able to classify them into glaucomatous and normal with high accuracy (AUC = 0.9017). The obtained retinal image synthesizer and the glaucoma classifier could then be used to generate an unlimited number of cropped retinal images with glaucoma labels.
引用
收藏
页码:2211 / 2218
页数:8
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