NDLI: Optic disc segmentation in fundus images using adversarial training

Content Provider	IET Digital Library
Author	Liu, Yang Fu, Dongmei Huang, Zhicheng Tong, Hejun
Abstract	Glaucoma is one of the leading causes of blindness in the world. Optic disc segmentation is an indispensable step for automatic detection of glaucoma with fundus images. In this study, the authors propose an automatic optic disc segmentation approach using adversarial training. The improved ‘U-Net’ is used as the segmentation network to detect optic disc from fundus images, and then the authors add a ‘Patch-level’ adversarial network to enhance higher-order consistency between ground truth and the output from segmentation network, which further boosts the performance of segmentation network. In addition, a new loss function is designed to solve the problem of pixel-level class imbalance in small target region extraction of medical images. All these improvements have effectively increased the segmentation accuracy on hard examples. Authors’ methods achieve Dice coefficient of 0.967 on Drishti-GS dataset and 0.951 on RIM-ONEv3 dataset, which outperform most of the existing methods.
Starting Page	375
Ending Page	381
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5922
Journal	IET Image Processing
Publisher Date	2018-11-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adversarial Training Automatic Detection Automatic Optic Disc Segmentation Approach Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Computer Vision And Image Processing Technique Diseases Drishti-GS Dataset Eye Feature Extraction Fundus Image Glaucoma Image Enhancement Image Segmentation Medical Image Medical Image Processing Optical And Laser Radiation Optical, Image And Video Signal Processing Patch-level Adversarial Network Patient Diagnostic Method And Instrumentation RIM-ONEv3 Dataset Segmentation Accuracy Segmentation Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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