Segmentation of OCT Biomarkers in Retinal Diseases using Deep Learning methods

This thesis focuses on the segmentation of Optical Coherence Tomography (OCT) images are used to assist in the diagnosis and treatment of several retinal diseases, such as age-related macular degeneration (AMD) and vitreomacular interface disorders (VID). The study uses the U-Net architecture for AMD to perform multiclass segmentation of biomarkers, specifically drusen, scars, and fluids. The performance of the standard U-Net is compared with various advanced U-Net architectures to determine the most effective model. Similarly, for VID, the segmentation task focuses on identifying macular holes, and the results from the U-Net model are compared with those from more sophisticated U-Net variants. Through extensive experimentation and analysis, this research aims to enhance the accuracy and reliability of OCT image segmentation, contributing to better diagnostic tools for these vision-threatening conditions.