Joint optic disc and cup segmentation based on densely connected

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.

Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network, BMC Ophthalmology

Diagnostics, Free Full-Text

Symmetry, Free Full-Text

Joint optic disk and cup segmentation for glaucoma screening using a region- based deep learning network

Joint optic disk and cup segmentation for glaucoma screening using a region- based deep learning network

Introduction of OD and OC area. (a) shows the structure of the optic

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. - Abstract - Europe PMC

Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network, BMC Ophthalmology

International Journal of Imaging Systems and Technology, IMA

a) Retinography and (b) detailed view of the optic disc.

Training and validation loss on the DRISHTI-GS dataset. The blue line

Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture - ScienceDirect