FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images

TL;DR

FunduSegmenter leverages the RETFound foundation model with novel modules to achieve state-of-the-art joint optic disc and optic cup segmentation in retinal fundus images, demonstrating strong accuracy and generalization.

Contribution

This work introduces FunduSegmenter, the first adaptation of RETFound for joint OD and OC segmentation, with novel modules that enhance performance and can extend to other foundation models.

Findings

Achieved an average Dice score of 90.51%, outperforming baselines.

Model showed about 3% higher results than the best baseline in external tests.

Demonstrated strong stability and generalization across datasets.

Abstract

Purpose: This study introduces the first adaptation of RETFound for joint optic disc (OD) and optic cup (OC) segmentation. RETFound is a well-known foundation model developed for fundus camera and optical coherence tomography images, which has shown promising performance in disease diagnosis. Methods: We propose FunduSegmenter, a model integrating a series of novel modules with RETFound, including a Pre-adapter, a Decoder, a Post-adapter, skip connections with Convolutional Block Attention Module and a Vision Transformer block adapter. The model is evaluated on a proprietary dataset, GoDARTS, and four public datasets, IDRiD, Drishti-GS, RIM-ONE-r3, and REFUGE, through internal verification, external verification and domain generalization experiments. Results: An average Dice similarity coefficient of 90.51% was achieved in internal verification, which outperformed all baselines, some substantially (nnU-Net: 82.91%; DUNet: 89.17%; TransUNet: 87.91%). In all external verification experiments, the average results were about 3% higher than those of the best baseline, and our model was also competitive in domain generalization. Conclusions: This study explored the potential of the latent general representations learned by RETFound for OD and OC segmentation in fundus camera images. Our FunduSegmenter generally outperformed state-of-the-art baseline methods. The proposed modules are general and can be extended to fine-tuning other foundation models. Translational Relevance: The model shows strong stability and generalization on both in-distribution and out-of-distribution data, providing stable OD and OC segmentation. This is an essential step for many automated tasks, from setting the accurate retinal coordinate to biomarker discovery. The code and trained weights are available at: https://github.com/JusticeZzy/FunduSegmenter.