3D deep convolutional neural network for predicting neurosensory retinal thickness map from spectral domain optical coherence tomography volumes

Oscar J. Perdomo, Hernan A. Rios, Francisco J. Rodríguez, Fabio A. González

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Age-related macular degeneration is a common cause of vision loss in people aging 55 and older. The condition affects the light-sensing cells in the macula limiting the sharp and central vision. On the other hand, Spectral Domain Optical Coherence Tomography (SD-OCT) allow highlighting abnormalities and thickness in the retinal layers which are useful for age-related macular degeneration diagnosis and follow up. The Neurosensory retina (NSR) map is defined as the thickness between the inner limiting membrane layer and the inner aspect of the retinal pigment epithelium complex. Additionally, the NSR map has been used to differentiate between healthy and subjects with macular problems, but the plotting of the retinal thickness map depends critically on additional manufacturer interpretation software to automatically drawing. Therefore, this paper presents an end-to-end 3D convolutional neural network to automatically extract nine thickness mean values to draw the NSR map from an SD-OCT.

Original languageEnglish (US)
Title of host publication14th International Symposium on Medical Information Processing and Analysis
EditorsNatasha Lepore, Eduardo Romero, Jorge Brieva
PublisherSPIE
ISBN (Electronic)9781510626058
DOIs
StatePublished - 2018
Externally publishedYes
Event14th International Symposium on Medical Information Processing and Analysis, SIPAIM 2018 - Mazatlan, Mexico
Duration: Oct 24 2018Oct 26 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10975
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference14th International Symposium on Medical Information Processing and Analysis, SIPAIM 2018
Country/TerritoryMexico
CityMazatlan
Period10/24/1810/26/18

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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