  Glaucoma is a chronic visual disease that may cause permanent irreversible
blindness. Measurement of the cup-to-disc ratio (CDR) plays a pivotal role in
the 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. Existing
CNN-based segmentation frameworks resort to building deep encoders with
aggressive downsampling layers, which suffer from a general limitation on
modeling explicit long-range dependency. To this end, in this paper, we propose
a new segmentation pipeline, called UT-Net, availing the advantages of U-Net
and transformer both in its encoding layer, followed by an attention-gated
bilinear fusion scheme. In addition to this, we incorporate Multi-Head
Contextual attention to enhance the regular self-attention used in traditional
vision transformers. Thus low-level features along with global dependencies are
captured in a shallow manner. Besides, we extract context information at
multiple encoding layers for better exploration of receptive fields, and to aid
the model to learn deep hierarchical representations. Finally, an enhanced
mixing loss is proposed to tightly supervise the overall learning process. The
proposed model has been implemented for joint OD and OC segmentation on three
publicly available datasets: DRISHTI-GS, RIM-ONE R3, and REFUGE. Additionally,
to validate our proposal, we have performed exhaustive experimentation on
Glaucoma detection from all three datasets by measuring the Cup to Disc Ratio
(CDR) value. Experimental results demonstrate the superiority of UT-Net as
compared to the state-of-the-art methods.
