ARS-FORMER: A NOVEL SEGFORMER ARCHITECTURE FOR OPTIC DISC AND OPTIC CUP SEGMENTATION

The Cup-to-Disc Ratio (CDR) is a crucial diagnostic measure for glaucoma, comparing the diameters of the Optic Disc (OD) and Optic Cup (OC) in retinal images. Manual CDR measurement can be inaccurate due to the irregular shapes of these structures, leading to potential diagnostic errors. Automated semantic segmentation offers a faster, more accurate alternative for CDR assessment. This study aims to develop a novel architecture, ARS-Former (Attention-Refinement SegFormer), to improve the semantic segmentation of retinal images. This study focuses on improving the segmentation of small, complex structures, such as the OD and OC. ARS-Former is a modified SegFormer that integrates a Convolutional Block Attention Module (CBAM) into the encoder skip connections and adds a Refinement Block-ASPP in the decoder. The CBAM integration enhances the representation of essential features, channel-wise and spatially, ensuring that the decoder receives more relevant information. The Refinement Block-ASPP improves the model’s ability to capture multiscale context and refine object boundaries, thereby improving segmentation. Experimental results on the Drishti_GS dataset demonstrate that ARS-Former achieves 99% accuracy, 98% precision, 97.3% recall, 95.4% IoU, 97.6% DSC, and 98% ROC. These results indicate the architecture’s excellent performance and robustness in segmenting the OD and OC. The proposed method demonstrates excellent performance and robustness in OD and OC segmentation. However, further improvements are required, particularly in handling lower-quality images, to enhance robustness across diverse imaging conditions and ensure applicability in actual clinical practice.