ArticleJournal of biomedical optics2020
High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning.
Article in Journal of biomedical optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography.Scientific reports · 2024Article
- Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network.Biomedical optics express · 2023Article
- Multi-Scale Reconstruction of Undersampled Spectral-Spatial OCT Data for Coronary Imaging Using Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Choroidal layer segmentation in OCT images by a boundary enhancement network.Frontiers in cell and developmental biology · 2022Article
- A review of generative adversarial network applications in optical coherence tomography image analysis.Journal of optometry · 2022Review
- The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network.Scientific reports · 2021Article
- Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification.Biomedical optics express · 2021Article
- Synthetic polarization-sensitive optical coherence tomography by deep learning.NPJ digital medicine · 2021Article
- Super-resolution technology to simultaneously improve optical & digital resolution of optical coherence tomography via deep learning.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
significanceReducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images.
aimWe propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition. APPROACH: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition.
resultsExtensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions.
conclusionsOur work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings.
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