• deep learning
• machine learning
• optical coherence tomography
• retinal layer
• segmentation
• segmentazione

This thesis studies the approaches for the segmentation of retinal layers in Optical Coherence Tomography (OCT) scans. It proposes an overview of the technology and the medical applications of these systems, with a focus on the advantages of intraoperative systems and, in particular, the added progress of having an automatic retinal layers' segmentation algorithm. It has been proved in these last years, that Deep Learning can deal efficiently computer vision tasks and there are examples in literature on using these recent methods to segment retinal layer on diagnostic OCT scans. In this thesis, it is presented a recap of the most recent and successful between these approaches and the technical ideas behind them. However, to the knowledge of the author, there are no segmentation algorithms applied to intraoperative systems. This study presents a model, specifically used for edge detection, in order to find two retinal layers, Inner Limiting Layer (ILM) and Retinal Pigment Epithelium (RPE), in intraoperative OCT B-scans. The task of segmenting retinal layers in intraoperative OCT scans is more challenging due to the noisy and (sometimes) unreliable nature of the images. Moreover, it is also presented an extension of the model with an adversarial extension, to avoid specific issues of the task and improve the outcome. Along with the results, the study shows a qualitative analysis for a more complete evaluation.