Abstract
BackgroundGlaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.MethodsIn this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.ResultsThe experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7% in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79% on the REFUGE dataset, respectively.ConclusionsThe promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.
Highlights
Glaucoma is an eye disease that causes vision loss and even blindness
The aims of this paper are as follow: (1) First, we aim at exploring an optic disc (OD) location method based on deep learning to solve the problem of instability location of traditional hand-crafted design feature methods due to the change of fundus image quality and the influence of pathological areas; (2) Second, we aim at exploring a deeper and wider convolution neural network (CNN) model structure to obtain richer and more complex fine-grained features in fundus images, so that the model can perform better OD and optic cup (OC) segmentation results, especially in the more difficult OC segmentation task
We compared the performance of our method with some state-of-theart deep learning-based methods
Summary
Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Glaucoma is an eye disease that damages the optic nerves and causes irreversible vision loss [1]. For ONH evaluation, cup to disc ratio (CDR), means optic nerve rim to disc ratio in diameters, is one of the most important indicators for glaucoma screening and diagnosis. Accurate segmentation of optic disc (OD) and optic cup (OC) is essential for the calculation of CDR. Segmenting the optic disc and optic cup is the preliminary step in CDR measurement and Glaucoma assessment. Many works have been proposed to segment the optic disc and cup from the fundus image to assist clinicians to diagnose glaucoma more effectively
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