ArticleBiomedical optics express2022
Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A 3-dimensional Resnet model for assessment of drug efficacy in 3D cancer models using optical coherence tomography.PloS one · 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
- Non-destructive viability assessment of cancer cell spheroids using dynamic optical coherence tomography with trypan blue validation.Biomedical optics express · 2024Article
- Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model.Biomedical optics express · 2024Article
- Erratum: Deep convolutional neural networks-based scatterer density and resolution estimators in optical coherence tomography: erratum.Biomedical optics express · 2024Article
- Label-free visualization and quantification of the drug-type-dependent response of tumor spheroids by dynamic optical coherence tomography.Scientific reports · 2024Article
- Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.Journal of personalized medicine · 2023Review
- Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network.Biomedical optics express · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an
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