No Cover Image

Journal article 612 views 78 downloads

An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images

Zhijie Liu, Yuanqiong Chen, Xiaohua Xiang, Zhan Li, Bolin Liao, Jianfeng Li

Mathematics, Volume: 10, Issue: 22, Start page: 4288

Swansea University Author: Zhan Li

  • 62097.pdf

    PDF | Version of Record

    © 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

    Download (3.12MB)

Check full text

DOI (Published version): 10.3390/math10224288

Abstract

Glaucoma is the second-most-blinding eye disease in the world and accurate segmentation of the optic disc (OD) and optic cup (OC) is essential for the diagnosis of glaucoma. To solve the problems of poor real-time performance, high algorithm complexity, and large memory consumption of fundus segment...

Full description

Published in: Mathematics
ISSN: 2227-7390
Published: MDPI AG 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62097
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Glaucoma is the second-most-blinding eye disease in the world and accurate segmentation of the optic disc (OD) and optic cup (OC) is essential for the diagnosis of glaucoma. To solve the problems of poor real-time performance, high algorithm complexity, and large memory consumption of fundus segmentation algorithms, a lightweight segmentation algorithm, GlauNet, based on convolutional neural networks, is proposed. The algorithm designs an efficient feature-extraction network and proposes a multiscale boundary fusion (MBF) module, which greatly improves the segmentation efficiency of the algorithm while ensuring segmentation accuracy. Experiments show that the algorithm achieves Dice scores of 0.9701/0.8959, 0.9650/0.8621, and 0.9594/0.8795 on three publicly available datasets—Drishti-GS, RIM-ONE-r3, and REFUGE-train—for both the optic disc and the optic cup. The number of model parameters is only 0.8 M, and it only takes 13 ms to infer an 800 × 800 fundus image on a GTX 3070 GPU.
Keywords: convolutional neural network; optic disc and cup segmentation; glaucoma screening; medical auxiliary diagnosis
College: Faculty of Science and Engineering
Funders: The work was supported by National Natural Science Foundation of China (61962023).
Issue: 22
Start Page: 4288

Similar Items