Citation: Liu, Z.; Chen, Y.; Xiang, X.;
Li, Z.; Liao, B.; Li, J. An End-to-End
Real-Time Lightweight Network for
the Joint Segmentation of Optic Disc
and Optic Cup on Fundus Images.
Mathematics 2022, 10, 4288. https://
doi.org/10.3390/math10224288
Academic Editor: Jakub Nalepa
Received: 11 October 2022
Accepted: 14 November 2022
Published: 16 November 2022
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Article
An End-to-End Real-Time Lightweight Network for the Joint
Segmentation of Optic Disc and Optic Cup on Fundus Images
Zhijie Liu
1,2
, Yuanqiong Chen
1,3
, Xiaohua Xiang
4
, Zhan Li
5
, Bolin Liao
1
and Jianfeng Li
1,2,
*
1
School of Computer Science and Engineering, Jishou University, Jishou 416000, China
2
School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China
3
School of Computer Science and Engineering, Central South University, Changsha 410083, China
4
Department of Computer Science, Xiangxi National Vocational and Technical College, Jishou 416000, China
5
Department of Computer Science, Swansea University, Swansea SA1 8EN, UK
* Correspondence: ljf@jsu.edu.cn
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
MSC: 68T07
1. Introduction
Glaucoma is a chronic eye disease that causes irreversible damage to vision. Patients
with glaucoma suffer damage to the optic nerve due to increased intraocular pressure (IOP)
caused by an imbalance between fluid production and drainage in the eye. The vertical
cup-to-disk ratio (
CDR
) is one of the commonly used indicators for clinical screening of
glaucoma. Usually, a
CDR
greater than 0.65 [
1
] is diagnosed as glaucoma. Figure 1 is
a sample map of normal eyes and glaucoma fundus and the corresponding annotation
map of the OD and OC. Accurate segmentation of the OD and the OC is essential for
accurate
CDR
acquisition. At present, the clinical diagnosis of glaucoma is mainly made by
ophthalmologists through manual diagnosis, which is somewhat subjective—results vary
greatly between doctors and are inefficient. With the rapid development of information
technology, rapid advances in medically assisted diagnostic techniques have been made,
making large-scale glaucoma screening possible.
Semantic segmentation is a fundamental task of computer vision. With the great
success of deep learning in the field of computer vision, algorithms based on deep learning
have improved in efficiency and accuracy compared with traditional machine learning
algorithms, providing new ideas for the development of medical image-assisted diagnosis
technology. In recent years, segmentation algorithms for OD and OC based on deep
learning have emerged one after another. The following are some excellent algorithms
based on convolutional neural networks: The authors of [
2
] proposed an attention U-Net
Mathematics 2022, 10, 4288. https://doi.org/10.3390/math10224288 https://www.mdpi.com/journal/mathematics
