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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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OCT Signal Enhancement with Deep Learning.

Georgios Lazaridis1, Marco Lorenzi2, Jibran Mohamed-Noriega3

  • 1NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Ophthalmology. Glaucoma
|October 18, 2020
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Summary
This summary is machine-generated.

Deep learning enhanced time-domain OCT images, improving signal-to-noise ratio and agreement with spectral-domain OCT. This advancement strengthens the prediction of glaucoma progression using retinal nerve fiber layer thickness changes.

Keywords:
Deep learningGlaucomaImage analysisOCTVisual fields

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Spectral-domain optical coherence tomography (SD-OCT) offers superior signal-to-noise ratio (SNR) compared to time-domain OCT (TD-OCT).
  • Improving TD-OCT image quality is crucial for accurate glaucoma diagnosis and monitoring.

Purpose of the Study:

  • To evaluate deep learning methods for enhancing TD-OCT images.
  • To assess if enhanced TD-OCT images can achieve SNR comparable to SD-OCT.
  • To determine if enhanced TD-OCT improves glaucoma progression detection.

Main Methods:

  • An ensemble of generative adversarial networks (GANs) was trained on paired TD-OCT and SD-OCT images.
  • TD-OCT images were processed to synthesize SD-OCT images.
  • Bayesian fusion was used for image segmentation.
  • Bland-Altman analysis assessed agreement in retinal nerve fiber layer thickness (RNFLT) measurements.
  • Cox regression modeled the association between RNFLT change and visual field progression.

Main Results:

  • Image enhancement significantly improved agreement between TD-OCT and SD-OCT RNFLT measurements (95% limits of agreement: 8.11 to -6.73 for synthesized SD-OCT vs. SD-OCT).
  • The significance of RNFLT change differences between treatment and placebo arms in the UKGTS trial was enhanced with synthesized SD-OCT (P=0.0017).
  • Synthesized SD-OCT RNFLT slope was a stronger predictor of visual field progression (Hazard Ratio: 1.24, P=0.011).

Conclusions:

  • Deep learning-based image enhancement effectively improves TD-OCT image quality.
  • Synthesized SD-OCT images demonstrate better agreement with actual SD-OCT images.
  • Enhanced TD-OCT imaging improves the detection of glaucoma progression and its prediction of visual field changes.