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Pathological myopia classification with simultaneous lesion segmentation using deep learning.

Ruben Hemelings1, Bart Elen2, Matthew B Blaschko3

  • 1Research Group Ophthalmology, KU Leuven, Herestraat 49, 3000 Leuven, Belgium; VITO NV, Boeretang 200, 2400 Mol, Belgium.

Computer Methods and Programs in Biomedicine
|January 7, 2021
PubMed
Summary
This summary is machine-generated.

Convolutional neural networks accurately detect pathological myopia (PM) and segment associated lesions in fundus images. This automated approach aids in preventing blindness by identifying early signs of PM and its related ocular damage.

Keywords:
Pathological myopiaconvolutional neural networkfovea localizationfundus imageglaucomaperipapillary atrophyretinal detachment

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pathological myopia (PM) is a significant cause of blindness, affecting up to 3% globally.
  • Rising myopia prevalence worldwide necessitates early and automated detection methods.
  • Automated detection from fundus images can help prevent vision loss.

Purpose of the Study:

  • To evaluate convolutional neural networks (CNNs) for detecting PM.
  • To assess CNNs for segmenting myopia-induced lesions in fundus images.
  • To utilize a new reference dataset for PM analysis.

Main Methods:

  • Developed CNNs for simultaneous PM classification and lesion segmentation.
  • Integrated domain knowledge using an Optic Nerve Head (ONH)-based enhancement.
  • Employed segmentation for fovea localization, a novel approach.

Main Results:

  • Achieved an AUC of 0.9867 for PM detection.
  • Demonstrated a Euclidean distance of 58.27 pixels for fovea localization.
  • Obtained high Dice and F1 scores for optic disc (0.9303, 0.9869), retinal atrophy (0.8001, 0.9135), and retinal detachment (0.8073, 0.7059).

Conclusions:

  • Successfully developed a method for simultaneous PM classification and lesion segmentation.
  • The approach received an award at the IEEE International Symposium on Biomedical Imaging.
  • This work can assist in differentiating between glaucomatous and highly myopic eyes.