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The effect of optical degradation from cataract using a new Deep Learning optical coherence tomography segmentation

Davide Allegrini1, Raffaele Raimondi2, Tania Sorrentino3

  • 1Eye Center, Humanitas Gavazzeni-Castelli, Bergamo, Italy.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie
|October 16, 2023
PubMed
Summary

A free online Deep Learning tool accurately segmented retinal OCT images, demonstrating robustness even with cataract-induced image noise. This tool shows promise for clinical use in evaluating retinal health.

Keywords:
Cataract optical degradationDeep LearningOCTOptical coherence tomographySegmentation algorithm

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical Coherence Tomography (OCT) is crucial for retinal imaging.
  • Accurate segmentation of retinal layers is essential for quantitative analysis.
  • Deep Learning (DL) offers potential for automated image segmentation.

Purpose of the Study:

  • To evaluate the accuracy of a free online Deep Learning tool for retinal OCT segmentation.
  • To assess the tool's performance with image noise from cataracts.

Main Methods:

  • Retinal OCT images were acquired using Spectralis SD-OCT.
  • Segmentation was performed using the Relayer online DL tool.
  • Segmentations were analyzed in MATLAB.

Main Results:

  • Excellent agreement was found between ETDRS measurements from Relayer and other algorithms.
  • Relayer's DL-based segmentation generally appeared more accurate.
  • The tool showed robustness despite optical degradation from media opacities.

Conclusions:

  • The Relayer online DL tool demonstrates good performance for retinal OCT segmentation.
  • The tool is promising for clinical applications in healthy retinas.
  • It is robust to image noise caused by media opacities like cataracts.