Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina

Diagnosis and treatment in ophthalmology depend on modern retinal imaging by optical coherence tomography (OCT). The recent staggering results of machine learning in medical imaging have inspired the development of automated segmentation methods to identify and quantify pathological features in OCT scans. These models need to be sensitive to image features defining patterns of interest, while remaining robust to differences in imaging protocols. A dominant factor for such image differences is the type of OCT acquisition device. In this paper, we analyze the ability of recently developed unsupervised unpaired image translations based on cycle consistency losses (cycleGANs) to deal with image variability across different OCT devices (Spectralis and Cirrus) ...


Información adicional

País:     ---

Autor(es):   

  • David Edmundo Romo
  • Philipp Seebock
  • José Ignacio Orlando
  • Bianca S. Gerendas
  • Sebastian M. Waldstein
  • Ursula Schmidt-Erfurth
  • Hrvoje Bogunovi?

Año:     2020

ISSN:    ---

Revista:    Biomedical Optics Express

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