ArticleJournal of biophotonics2020
Optical coherence tomography image denoising using a generative adversarial network with speckle modulation.
Article in Journal of biophotonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 74 citations in OpenAlex.
- A Joint Model for Macular Edema Analysis in Optical Coherence Tomography Images Based on Image Enhancement and Segmentation.BioMed research international · 2021Trial
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Deep-learning-based optical coherence tomography reconstruction for high-speed and contrast morphology and vasculature imaging.Journal of biomedical optics · 2026Article
- Application of generative adversarial networks in the restoration of blurred optical coherence tomography images caused by optical media opacity in eyes.BMJ open ophthalmology · 2025Article
- Label-free evaluation of mouse embryo quality using time-lapse bright field and optical coherence microscopy.Communications biology · 2025Article
- Semi-supervised assisted multi-task learning for oral optical coherence tomography image segmentation and denoising.Biomedical optics express · 2025Article
- Complex conjugate removal in optical coherence tomography using phase aware generative adversarial network.Journal of biomedical optics · 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
- Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.Journal of personalized medicine · 2023Review
- Article
- Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma.Translational vision science & technology · 2023Article
- Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction.Journal of imaging · 2023Article
- Fast optical coherence tomography angiography image acquisition and reconstruction pipeline for skin application.Biomedical optics express · 2023Article
- Live 4D-OCT denoising with self-supervised deep learning.Scientific reports · 2023Article
- Methods and applications of full-field optical coherence tomography: a review.Journal of biomedical optics · 2022Review
- Real-time OCT image denoising using a self-fusion neural network.Biomedical optics express · 2022Article
- Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss.PeerJ. Computer science · 2022Article
- A review of generative adversarial network applications in optical coherence tomography image analysis.Journal of optometry · 2022Review
- Self-assembling human heart organoids for the modeling of cardiac development and congenital heart disease.Nature communications · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT denoising methods and other state-of-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.