Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network, BMC Medical Imaging

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.

Mathematics, Free Full-Text

A Novel Deep Learning Algorithm for Optical Disc Segmentation for

RMSDSC‐Net: A robust multiscale feature extraction with depthwise

Joint optic disc and optic cup segmentation based on boundary

Frontiers O-Net: A Novel Framework With Deep Fusion of CNN and

Joint optic disc and cup segmentation based on densely connected

Fully Convolutional Network and Visual Saliency-Based Automatic

PDF) Joint optic disc and cup segmentation based on densely

PDF) Joint optic disc and cup segmentation based on densely

PDF) Medical image segmentation using deep learning: A survey

PDF) Joint Optic Disc and Dup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

C2FTFNet: Coarse-to-fine transformer network for joint optic disc

Full article: Machine Learning and Deep Learning Techniques for