Paper
15 June 2020 Intensity-invariant scatterer density estimation for optical coherence tomography using deep convolutional neural network
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Abstract
A convolutional neural networks (CNN) based scatterer density estimator for optical coherence tomography (OCT) is presented. In order to train the OCT, small patches of OCT speckle image were numerically generated. In this numerical image generation, the imaging parameters including the resolutions, probe power, signal-to-noise ratio, and scatterer density were randomly defined. So, the CNN was trained to estimate the imaging parameters from the generated OCT image patch. The results showed that our CNN estimator can estimate the parameters from the OCT speckle images.
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Thitiya Seesan, Daisuke Oida, Kensuke Oikawa, Prathan Buranasiri, and Yoshiaki Yasuno "Intensity-invariant scatterer density estimation for optical coherence tomography using deep convolutional neural network", Proc. SPIE 11521, Biomedical Imaging and Sensing Conference 2020, 115210Q (15 June 2020); https://doi.org/10.1117/12.2573236
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KEYWORDS
Optical coherence tomography

Signal to noise ratio

Speckle

Convolutional neural networks

Speckle pattern

Laser beam diagnostics

Biological research

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