A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus Photographs

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Abstract
Importance: Glaucoma, a leading cause of blindness worldwide, depends on accurate optic nerve head assessment, particularly optic disc and cup segmentation, for diagnosis and monitoring. Deep learning (DL) models can automate these measurements, but models trained on smaller, site-specific datasets often fail to generalize. While larger, multi-site datasets help, data privacy concerns limit centralized training. Objective: To evaluate a federated learning (FL) framework with site-specific fine-tuning for optic disc and cup segmentation, aiming to match central model performance while preserving privacy and improving generalizability. Design: Comparative evaluation of three different approaches: (1) a central model trained on multi-site data, (2) site-specific local model training (3) standard FL models, against an FL with site-specific fine-tuning. Setting: Multicenter study incorporating nine publicly available datasets, representing varied clinical environments, populations, and imaging protocols. Participants: 5,550 color fundus photographs from at least 917 individuals across nine datasets includingboth routine care and research sources from 7 countries. Exposures: Optic disc and cup segmentationin color fundus photographs using training with local model, central model, standard FL, and FL with site-specific fine-tuning.. Main Outcomes and Measures: Segmentation accuracy measured by Dice score. Comparisons were labeled as performance “wins” or “losses” based on statistically significant differences via Wilcoxon signed-rank test (P < 0.05). Results: Site-specific fine-tuning of FL with site-specific fine tuning matched central model performance for cup segmentation across all sites (9/9) and for disc segmentation in most sites (7/9). Compared with site-specific local models, it preserved within-site performance (cup: 9/9; disc: 5/9) while substantially improving cross-site generalizability, achieving significant gains in 54.2% (39/72) of disc and 25.0% (18/72) of cup external-site evaluations, with no significant losses. Compared to standard FL pipelines, site-specific fine-tuning improved performance by 52% for disc and 26% for cup. Conclusions and Relevance: Site-specific fine-tuning within an FL framework effectively personalizes generalized models to local data distributions, achieving central-level performance without data sharing and enhancing cross-site robustness. This approach enables privacy-preserving, scalable AI deployment across heterogeneous clinical settings for reproducible and generalizable glaucoma assessment KEY POINTS: Question: How can we train an AI model to segment the optic cup and disc across multiple sites without sharing data, yet achieve performance comparable to a central model trained on pooled datasets? Findings: In this federated learning (FL) study of 5,550 fundus photographs from nine sites, a site-specific fine-tuning FL strategy matched the central model’s performance and outperformed other standard FL techniques, with notable gains in cross-site generalizability. Meaning: Site-specific fine-tuning effectively personalizes FL models to local data distributions, combining data privacy with robust, generalizable performance.

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