OCT Segmentation via Deep Learning: A Review of Recent Work

M. Pekala, N. Joshi, T. Y.Alvin Liu, N. M. Bressler, D. Cabrera DeBuc, P. Burlina

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


Optical coherence tomography (OCT) is an important retinal imaging method since it is a non-invasive, high-resolution imaging technique and is able to reveal the fine structure within the human retina. It has applications for retinal as well as neurological disease characterization and diagnostics. The use of machine learning techniques for analyzing the retinal layers and lesions seen in OCT can greatly facilitate such diagnostics tasks. The use of deep learning (DL) methods principally using fully convolutional networks has recently resulted in significant progress in automated segmentation of optical coherence tomography. Recent work in that area is reviewed herein.

Original languageEnglish (US)
Title of host publicationComputer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers
EditorsGustavo Carneiro, Shaodi You
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783030210731
StatePublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: Dec 2 2018Dec 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018


  • Neurodegenerative
  • OCT segmentation
  • retinal and vascular diseases

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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