Leveraging the RETFound foundation model for optic disc segmentation in retinal images

• URL: http://arxiv.org/abs/2508.11354v1
• Date: Fri, 15 Aug 2025 09:43:49 GMT
• Title: Leveraging the RETFound foundation model for optic disc segmentation in retinal images
• Authors: Zhenyi Zhao, Muthu Rama Krishnan Mookiah, Emanuele Trucco,
• Abstract summary: RETFound is a well-known foundation model (FM) developed for fundus camera and optical coherence tomography images.<n>We present the first adaptation of RETFound for optic disc segmentation, a ubiquitous and foundational task in retinal image analysis.
• Score: 1.1825096932487489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: RETFound is a well-known foundation model (FM) developed for fundus camera and optical coherence tomography images. It has shown promising performance across multiple datasets in diagnosing diseases, both eye-specific and systemic, from retinal images. However, to our best knowledge, it has not been used for other tasks. We present the first adaptation of RETFound for optic disc segmentation, a ubiquitous and foundational task in retinal image analysis. The resulting segmentation system outperforms state-of-the-art, segmentation-specific baseline networks after training a head with only a very modest number of task-specific examples. We report and discuss results with four public datasets, IDRID, Drishti-GS, RIM-ONE-r3, and REFUGE, and a private dataset, GoDARTS, achieving about 96% Dice consistently across all datasets. Overall, our method obtains excellent performance in internal verification, domain generalization and domain adaptation, and exceeds most of the state-of-the-art baseline results. We discuss the results in the framework of the debate about FMs as alternatives to task-specific architectures. The code is available at: [link to be added after the paper is accepted]

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