Deep convolutional neural networks-based scatterer density estimator in optical coherence tomography

Name: Thitiya Seesan

LCSH: Neural networks (Computer science)

LCSH: Light -- Speed

Abstract: We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density, lateral and axial resolutions, signal-to-noise ratio, and effective number of scatterers (i.e., 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. The experimental validation using scattering phantoms shows the reasonable estimations of the scatterer density and the resolutions. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found

King Mongkut's Institute of Technology Ladkrabang. Central Library

Address: BANGKOK

Email: Lifelong@kmitl.ac.th

Name: Prathan Buranasiri

Role: Thesis Advisor

Modified: 2566-05-24

Issued: 2023-05-24

วิทยานิพนธ์/Thesis

application/pdf

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Applied Physics

Grantor: King Mongkut's Institute of Technology Ladkrabang

©copyrights King Mongkut's Institute of Technology Ladkrabang

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