Deep learning based retinal OCT segmentation.

Journal: Computers in biology and medicine
Published Date: Sep 17, 2019

Abstract

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.

Authors

  • M Pekala
    Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
  • N Joshi
    Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
  • T Y Alvin Liu
    Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD.
  • N M Bressler
    Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • D Cabrera DeBuc
    Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
  • P Burlina
    Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA; Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: pburlin2@jh.edu.

Keywords

External Resources

Popular Topics
Recent Journals