Pathological myopia classification with simultaneous lesion segmentation using deep learning.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 28, 2020

Abstract

BACKGROUND AND OBJECTIVES: Pathological myopia (PM) is the seventh leading cause of blindness, with a reported global prevalence up to 3%. Early and automated PM detection from fundus images could aid to prevent blindness in a world population that is characterized by a rising myopia prevalence. We aim to assess the use of convolutional neural networks (CNNs) for the detection of PM and semantic segmentation of myopia-induced lesions from fundus images on a recently introduced reference data set.

Authors

  • Ruben Hemelings
    Research Group Ophthalmology, KU Leuven, Kapucijnenvoer 33, 3000 Leuven, Belgium; ESAT-PSI, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium; VITO NV, Boeretang 200, 2400 Mol, Belgium.
  • Bart Elen
    VITO NV, Boeretang 200, 2400 Mol, Belgium.
  • Matthew B Blaschko
  • Julie Jacob
    Ophthalmology Department, UZ Leuven, Herestraat 49, 3000 Leuven, Belgium.
  • Ingeborg Stalmans
    Research Group Ophthalmology, KU Leuven, Kapucijnenvoer 33, 3000 Leuven, Belgium.
  • Patrick De Boever
    Hasselt University, Agoralaan building D, 3590 Diepenbeek, Belgium; VITO NV, Boeretang 200, 2400 Mol, Belgium. Electronic address: patrick.deboever@vito.be.

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