Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.

Journal: Translational vision science & technology
Published Date: Sep 1, 2022

Abstract

PURPOSE: To evaluate the feasibility of automated segmentation of pigmented choroidal lesions (PCLs) in optical coherence tomography (OCT) data and compare the performance of different deep neural networks.

Authors

  • Philippe Valmaggia
    Institute of Molecular and Clinical Ophthalmology Basel (IOB), 4031, Basel, Switzerland.
  • Philipp Friedli
    Supercomputing Systems AG, Zürich, Switzerland.
  • Beat Hörmann
    Supercomputing Systems AG, Zürich, Switzerland.
  • Pascal Kaiser
    Biotechnologie & Physik, Supercomputing Systems, Zürich, Switzerland.
  • Hendrik P N Scholl
    Department of Ophthalmology, University of Basel, Basel, Switzerland.
  • Philippe C Cattin
    Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland.
  • Robin Sandkühler
    Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
  • Peter M Maloca
    OCTlab, Department of Ophthalmology, University of Basel, Basel, Switzerland.

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