Deep learning based retinal OCT segmentation

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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


We look at the recent application of deep learning (DL) methods in automated fine-grained segmentation of spectral domain optical coherence tomography (OCT) images of the retina. We describe a new method combining fully convolutional networks (FCN) with Gaussian Processes for post processing. We report performance comparisons between the proposed approach, human clinicians, and other machine learning (ML) such as graph based approaches. The approach is demonstrated on an OCT dataset consisting of mild non-proliferative diabetic retinopathy from the University of Miami. The method is shown to have performance on par with humans, also compares favorably with the other ML methods, and appears to have as small or smaller mean unsigned error (equal to 1.06), versus errors ranging from 1.17 to 1.81 for other methods, and compared with human error of 1.10.

Original languageEnglish (US)
Article number103445
JournalComputers in Biology and Medicine
StatePublished - Nov 2019


  • Fully convolutional networks
  • Gaussian process regression
  • Neurodegenerative
  • OCT segmentation
  • Retinal and vascular diseases

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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