Fundus Image Segmentation

Fundus image segmentation, the automated identification and delineation of structures within retinal images, is crucial for early disease detection and diagnosis. Current research emphasizes improving segmentation accuracy and robustness across diverse datasets using advanced architectures like U-Net variations, transformers (e.g., Swin Transformer), and MLP-based models, often incorporating techniques like domain adaptation and uncertainty quantification to address data variability and annotation challenges. These advancements hold significant promise for improving the efficiency and accuracy of ophthalmological screening and diagnosis, potentially leading to earlier interventions and better patient outcomes.