ArticleBiomedical optics express2024
Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Robust image quality evaluation in optical coherence tomography of skin using global, region-independent metrics.Scientific reports · 2026Article
- Dynamic optical coherence tomography algorithm for label-free assessment of swiftness and occupancy of intratissue moving scatterers.Biomedical optics express · 2026Article
- Experimental and numerical investigation of wavelength and resolution dependency of dynamic optical coherence tomography signals.Biomedical optics express · 2025Article
- Dynamic-OCT simulation framework based on mathematical models of intratissue dynamics, image formation, and measurement noise.Biomedical optics express · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and
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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 Socratic graph.