One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation

Retinal fluids, detectable through optical coherence tomography (OCT), are key biomarkers for retinal diseases such as diabetic macular edema and age-related macular degeneration, guiding treatment decisions and monitoring response to therapy. Automated segmentation of retinal fluids could support large-scale clinical research and the development of clinical decision support tools. Recent ophthalmic foundation models trained on massive retinal imaging datasets show promise across many downstream tasks, including disease risk prediction and biomarker segmentation, but their performance relative to task-specific models for specialized clinical tasks remains unclear. We compared a task-specific segmentation model (RetiFluidNet) and an ophthalmic foundation model (VisionFM) using a standard benchmarking dataset containing 4,248 OCT images from 48 patients with three retinal diseases. Models were evaluated using three-fold cross-validation and assessed for pixel-level segmentation accuracy and patient-level fluid burden estimation. The task-specific model achieved higher segmentation performance and more consistent fluid quantification across devices. These findings suggest that, for retinal fluid segmentation, specialized task-specific models currently remain more reliable than general-purpose foundation models, highlighting the need for targeted adaptation before clinical deployment.