Leveraging the RETFound foundation model for optic disc segmentation in retinal images
This work addresses the problem of accurate and efficient optic disc segmentation for retinal image analysis, demonstrating the potential of foundation models as alternatives to task-specific architectures, though it is incremental as it applies an existing model to a new task.
The authors adapted the RETFound foundation model for optic disc segmentation in retinal images, achieving about 96% Dice consistently across multiple datasets and outperforming state-of-the-art segmentation-specific baselines with minimal task-specific training.
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]