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Background 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. 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. Methods In 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. Results The 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. Conclusions The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.
Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning
C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation - ScienceDirect
Fully Convolutional Network and Visual Saliency-Based Automatic Optic Disc Detection in Retinal Fundus Images
Mathematics, Free Full-Text
International Journal of Imaging Systems and Technology, IMA
Block diagram of the proposed optic disc and cup segmentation system.
A spatial-aware joint optic disc and cup segmentation method - ScienceDirect
Fully Convolutional Network and Visual Saliency-Based Automatic Optic Disc Detection in Retinal Fundus Images
Result of OD and OC segmentation on the REFUGE dataset.Top Three
C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation - ScienceDirect
International Journal of Imaging Systems and Technology, IMA
Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture
Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network, BMC Ophthalmology
C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation - ScienceDirect
