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Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning

arXiv 2301.00504 ↗First posted 2 Jan 2023eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 2 Jan 2023.

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14 of 30 references.

References · 14

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Improved Techniques for Training GANscited by 2,938
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  4. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  5. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  6. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
  7. 2013Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networkscited by 84
  8. 2016Amortised MAP Inference for Image Super-resolutioncited by 68
  9. 2019On Stabilizing Generative Adversarial Training with Noisecited by 25
  10. 2020Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learningcited by 7
  11. 2021Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral datacited by 6
  12. 2019Assessment of Generative Adversarial Networks Model for Synthetic Optical Coherence Tomography Images of Retinal Disorderscited by 5
2 more
  1. 2020OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Headcited by 4
  2. 2021Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomographycited by 1

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