AUTOMATIC OPTIC DISK AND CUP SEGMENTATION OF FUNDUS IMAGES USING DEEP LEARNING

被引：0

作者：

Edupuganti, Venkata Gopal ^{[1
]}

Chawla, Akshay ^{[1
]}

Kale, Amit ^{[1
]}

机构：

[1] Bosch Res & Technol Ctr India, Bangalore 560095, Karnataka, India

来源：

2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2018年

关键词：

Semantic Segmentation; Neural Networks; Glaucoma; Deep Learning; Fundus Imaging;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Automatic segmentation of optic disk (OD) and cup regions in fundus images is essential in deriving clinical parameters, such as, cup-to-disk ratio (CDR), to assist glaucoma diagnosis. This paper presents a deep learning system using fully convolutional neural networks (FCN) to perform such segmentation, discusses various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions while training the neural network. Experimental evaluations on Drishti-GS dataset demonstrate that the presented method achieves comparable and superior F-score to prior work on optic disk and cup segmentation, respectively.

引用

页码：2227 / 2231

页数：5

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