ArticleScientific reports2019
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed, 4 citations in OpenAlex.
- Automated Feature Segmentation of Ultra-Widefield OCT Images.Ophthalmology science · 2026Article
- Independent Effects of Axial Length and Intraocular Pressure on the Highly Myopic Optic Nerve Head.Investigative ophthalmology & visual science · 2025Article
- Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation.Scientific reports · 2025Article
- Physics-guided deep learning-based real-time image reconstruction of Fourier-domain optical coherence tomography.Biomedical optics express · 2024Article
- Probabilistic volumetric speckle suppression in OCT using deep learning.Biomedical optics express · 2024Article
- Exploring single-mode VCSEL wavelength tuning for low-cost 3D optical coherence tomography and OCT angiography.Biomedical optics express · 2024Article
- Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography.Scientific reports · 2024Article
- Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review.Journal of imaging · 2024Review
- Denoising OCT videos based on temporal redundancy.Scientific reports · 2024Article
- Unpaired intra-operative OCT (iOCT) video super-resolution with contrastive learning.Biomedical optics express · 2024Article
- Article
- Deep learning network for parallel self-denoising and segmentation in visible light optical coherence tomography of the human retina.Biomedical optics express · 2023Article
- Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction.Journal of imaging · 2023Article
- SSN2V: unsupervised OCT denoising using speckle split.Scientific reports · 2023Article
- Live 4D-OCT denoising with self-supervised deep learning.Scientific reports · 2023Article
- A decision-support tool for funding health innovations at a tertiary academic medical center.International journal of technology assessment in health care · 2023Review
- Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction.Sensors (Basel, Switzerland) · 2023Article
- Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images.Frontiers in medicine · 2023Article
- MURIN: Multimodal Retinal Imaging and Navigated-laser-delivery for dynamic and longitudinal tracking of photodamage in murine models.Frontiers in ophthalmology · 2023Article
- Quantitative optical coherence microscopy of neuron morphology in human entorhinal cortex.Frontiers in neuroscience · 2023Article
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 'clean B-scans' (multi-frame B-scans; signal averaged), and their corresponding 'noisy B-scans' (clean B-scans + Gaussian noise), we were able to successfully denoise 1,552 unseen single-frame (without signal averaging) B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean signal to noise ratio (SNR) increased from 4.02 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean contrast to noise ratio (CNR) increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The mean structural similarity index (MSSIM) increased from 0.13 ± 0.02 (single frame) to 0.65 ± 0.03 (denoised) when compared with the corresponding multi-frame B-scans. Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.
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