@inbook{905a06e8d62e451293b8ad50fbcc8776,
title = "Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans",
abstract = "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.",
author = "Sanchez, {Yeison D.} and Bernardo Quijano and Padilla, {Fabio D.} and Oscar Perdomo and Gonz{\'a}lez, {Fabio A.}",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 16th International Symposium on Medical Information Processing and Analysis 2020 ; Conference date: 03-10-2020 Through 04-10-2020",
year = "2020",
month = nov,
day = "3",
doi = "10.1117/12.2579934",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva and Marius Linguraru",
booktitle = "16th International Symposium on Medical Information Processing and Analysis",
address = "United States",
}