Retinal diseases are a common cause of blindness around the world, early detection of clinical findings can help to avoid vision loss in patients. Optical coherence tomography images have been widely used to diagnose retinal diseases, due to the capacity to show in detail findings as drusen, hyperreflective foci, and intraretinal and subretinal fluids. The location of findings is vital to identify and follow-up the retinal disease. However, the detection and segmentation of these findings is not an easy task due to artifacts noise, and the time consuming even to experts ophthalmologist. This paper proposes a computational method based on deep learning to automatically identify fluids and hyperreflective foci as a tool to identify retinal diseases through the use of OCT images. The method was evaluated on a set of OCT images manually annotated by experts. The experimental results present a Dice coefficient of 0,4437 and 0,6245 in the segmentation task of fluids (intraretinal fluids and subretinal fluids) and hyperreflective foci, respectively.
