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Automatic Detection and Characterization of Biomarkers in OCT Images

  • Melinda Katona
  • Attila Kovács
  • László Varga
  • Tamás Grósz
  • József Dombi
  • Rózsa Dégi
  • László G. NyúlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

Optical Coherence Tomography (OCT) is one of the most advanced, non-invasive method of eye examination. Age-related macular degeneration (AMD) is one of the most frequent reasons of acquired blindness. Our aim is to develop automatic methods that can accurately identify and characterize biomarkers in OCT images, related to AMD. We present methods for quantizing hyperreflective foci (HRF) with deep learning. We also describe an algorithm for determining pigmentepithelial detachment (PED) and localizing outer retinal tubulation (ORT) that appears between the layers of the retina.

Keywords

Age-related macular degeneration Biomarker Pigmentepithelial detachment Hyperreflective foci Outer retinal tubulation Optical coherence tomography 

Notes

Acknowledgements

We would like to thank the NVIDIA Corporation for the donation of the Tesla K40 GPU used for this research. Tamás Grósz was supported by the ÚNKP-17-3 New National Excellence Programme of the Ministry of Human Capacities.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Melinda Katona
    • 1
  • Attila Kovács
    • 4
  • László Varga
    • 1
  • Tamás Grósz
    • 2
  • József Dombi
    • 3
  • Rózsa Dégi
    • 4
  • László G. Nyúl
    • 1
    Email author
  1. 1.Department of Image Processing and Computer GraphicsUniversity of SzegedSzegedHungary
  2. 2.MTA-SZTE Research Group on Artificial IntelligenceUniversity of SzegedSzegedHungary
  3. 3.Department of Computer Algorithms and Artificial IntelligenceUniversity of SzegedSzegedHungary
  4. 4.Department of OphthalmologyUniversity of SzegedSzegedHungary

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