Retinal Layer Segmentation

Retinal layer segmentation, the automated identification of different layers within optical coherence tomography (OCT) images of the retina, aims to improve the diagnosis and monitoring of ophthalmic diseases. Current research emphasizes developing accurate and efficient deep learning models, often employing variations of U-Net, transformer-based architectures, or hybrid 2D-3D convolutional neural networks, to address challenges like low image contrast, noise, and inter-device variability. These advancements focus on improving segmentation accuracy, handling 3D volumetric data for better anatomical coherence, and reducing computational demands for practical clinical deployment. Ultimately, improved retinal layer segmentation promises more precise quantitative analysis of retinal structure, leading to earlier and more accurate disease detection and management.