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Glaucoma is a chronic visual disease that may cause permanent irreversibleblindness. Measurement of the cup-to-disc ratio (CDR) plays a pivotal role inthe detection of glaucoma in its early stage, preventing visual disparities.Therefore, accurate and automatic segmentation of optic disc (OD) and optic cup(OC) from retinal fundus images is a fundamental requirement. ExistingCNN-based segmentation frameworks resort to building deep encoders withaggressive downsampling layers, which suffer from a general limitation onmodeling explicit long-range dependency. To this end, in this paper, we proposea new segmentation pipeline, called UT-Net, availing the advantages of U-Netand transformer both in its encoding layer, followed by an attention-gatedbilinear fusion scheme. In addition to this, we incorporate Multi-HeadContextual attention to enhance the regular self-attention used in traditionalvision transformers. Thus low-level features along with global dependencies arecaptured in a shallow manner. Besides, we extract context information atmultiple encoding layers for better exploration of receptive fields, and to aidthe model to learn deep hierarchical representations. Finally, an enhancedmixing loss is proposed to tightly supervise the overall learning process. Theproposed model has been implemented for joint OD and OC segmentation on threepublicly available datasets: DRISHTI-GS, RIM-ONE R3, and REFUGE. Additionally,to validate our proposal, we have performed exhaustive experimentation onGlaucoma detection from all three datasets by measuring the Cup to Disc Ratio(CDR) value. Experimental results demonstrate the superiority of UT-Net ascompared to the state-of-the-art methods.

UT-Net: Combining U-Net and Transformer for Joint Optic Disc and Cup Segmentation and Glaucoma Detection