OD/OC-semantic FPN: an enhanced optic cup and disc segmentation model in color fundus images using improved MaxViT and semantic feature pyramid network

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Abstract

Glaucoma, a leading cause of irreversible blindness, requires early detection to prevent progressive vision loss. Color fundus photography is a non-invasive and widely accessible modality for glaucoma screening; however, traditional manual interpretation is limited by subjectivity, time inefficiency, and inter-observer variability. This study proposes an optic disc (OD)/optic cup (OC)semantic feature pyramid network, a joint OD and OC segmentation model for glaucoma screening. The model extends the Semantic FPN architecture through three key enhancements: (1) a MaxViT backbone that incorporates multi-axis attention to reinforce local-global feature interaction and preserve boundary information during downsampling; (2) inception depthwise convolution modules embedded within MBConv blocks, which enables multi-scale convolution to expand receptive fields without compromising fine-grained details; (3) an optimized semantic FPN structure to improve the cross-scale feature alignment and multi-scale fusion. The proposed OD/OC-Semantic FPN was evaluated on five publicly available fundus image datasets (Drishti-GS, ORIGA, RIM-ONE DL, RIM-ONE-R3, and REFUGE), and its performance was compared against several state-of-the-art segmentation models (U-Net, DeepLabV3+, PSPNet, APCNet, semantic FPN-PoolFormer, and attention U-Net). The results show that the OD/OC-semantic FPN surpasses existing models across several metrics: dice coefficient, Mean Intersection over Union (mIoU), mean pixal accuracy (MPA), and classification accuracy, thus demonstrating superior structural precision for fundus analysis. Collectively, these results indicate that the OD/OC-Semantic FPN is a robust and generalizable tool for intelligent early detection of glaucoma.

Keywords

glaucoma / color fundus image / deep learning / transformer / segmentation model

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