A Transformer-Based Framework for OCT Cyst Segmentation

Optical Coherence Tomography is a widely used imaging modality for diagnosing and monitoring retinal diseases. Automated segmentation of cystoid spaces in retinal scans is crucial for early detection and personalized treatment planning. In this work, we propose a novel transformer-based framework that leverages SegFormer for accurate segmentation of cystoid regions in OCT images. The study evaluates the impact of model size and different augmentation strategies, such as geometric and color-based transformations, on the segmentation performance. Our experiments, conducted using the OCT-Cyst Segmentation Challenge dataset, achieve high scoring results, with the best configuration yielding a Dice score of 84.57. Additionally, we provide qualitative analyses to highlight the strengths and limitations of the framework in real-world scenarios. The results demonstrate the robustness and clinical relevance of transformer-based architectures in OCT cyst segmentation. The code associated with this manuscript is available at: https://github.com/unica-visual-intelligence-lab/OCT-Cyst-Segmentation.