Retinal Layer

Retinal layer analysis, primarily using optical coherence tomography (OCT) images, aims to accurately segment and quantify the different layers of the retina for diagnosing and monitoring various ophthalmic diseases. Current research focuses on improving segmentation accuracy using advanced deep learning architectures, such as transformers and convolutional neural networks (CNNs), often incorporating multi-scale feature extraction and 3D processing to capture the full structural context of the retina. These advancements address challenges like shadow artifacts from blood vessels and variations in image quality across different OCT devices, ultimately leading to more precise and reliable diagnostic tools for conditions like glaucoma and macular degeneration. The improved accuracy and efficiency of retinal layer analysis have significant implications for clinical practice, enabling earlier and more effective disease management.