Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans

Yeison D. Sanchez, Bernardo Quijano, Fabio D. Padilla, Oscar Perdomo, Fabio A. González

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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.

Original languageEnglish (US)
Title of host publication16th International Symposium on Medical Information Processing and Analysis
EditorsEduardo Romero, Natasha Lepore, Jorge Brieva, Marius Linguraru
PublisherSPIE
ISBN (Electronic)9781510639911
DOIs
StatePublished - Nov 3 2020
Event16th International Symposium on Medical Information Processing and Analysis 2020 - Lima, Virtual, Peru
Duration: Oct 3 2020Oct 4 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11583
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference16th International Symposium on Medical Information Processing and Analysis 2020
Country/TerritoryPeru
CityLima, Virtual
Period10/3/2010/4/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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