Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images

Glaucoma is one of the most common chronic diseases that may lead to irreversible vision loss. The number of patients with permanent vision loss due to glaucoma is expected to increase at an alarming rate in the near future. A considerable amount of research is being conducted on computer-aided diagnosis for glaucoma. Segmentation of the optic cup (OC) and optic disc (OD) is usually performed to distinguish glaucomatous and nonglaucomatous cases in retinal fundus images. However, the OC boundaries are quite non-distinctive; consequently, the accurate segmentation of the OC is substantially challenging, and the OD segmentation performance also needs to be improved. To overcome this problem, we propose two networks, separable linked segmentation network (SLS-Net) and separable linked segmentation residual network (SLSR-Net), for accurate pixel-wise segmentation of the OC and OD. In SLS-Net and SLSR-Net, a large final feature map can be maintained in our networks, which enhances the OC and OD segmentation performance by minimizing the spatial information loss. SLSR-Net employs external residual connections for feature empowerment. Both proposed networks comprise a separable convolutional link to enhance computational efficiency and reduce the cost of network. Even with a few trainable parameters, the proposed architecture is capable of providing high segmentation accuracy. The segmentation performances of the proposed networks were evaluated on four publicly available retinal fundus image datasets: Drishti-GS, REFUGE, Rim-One-r3, and Drions-DB which confirmed that our networks outperformed the state-of-the-art segmentation architectures.

Automated summary

Glaucoma, a common chronic disease causing irreversible vision loss, is the focus of research on computer-aided diagnosis. This study proposes two networks, SLS-Net and SLSR-Net, for accurate segmentation of the optic cup and optic disc in retinal fundus images. By minimizing spatial information loss and using external residual connections, these networks achieve high segmentation accuracy.