Quantitative scatterer density estimator to characterize tissue-based phantom in optical coherence tomography

Simple item page
dc.contributor.authorThitiya Seesan
dc.contributor.authorIbrahim G. Abd El-Sadek
dc.contributor.authorPradipta Mukherjee
dc.contributor.authorKensuke Oikawa
dc.contributor.authorPrathan Buranasiri
dc.contributor.authorYoshiaki Yasuno
dc.date.accessioned2026-05-08T19:23:02Z
dc.date.issued2022-3-7
dc.description.abstractWe will present a deep convolutional neural network (DCNN) based estimators for optical coherence tomography (OCT). The DCNNs analyze local OCT speckle patterns and estimate the sample’s scatterer density and OCT resolutions. This estimator is intensity invariant, i.e., it does not use the net signal strength of OCT even to estimate the scatterer density. The DCNN is trained by a huge training dataset that was generated by a simple simulator of OCT imaging. This method is validated either by scattering phantom and in vitro tumor spheroid, and good accuracies of the estimation were shown.
dc.identifier.doi10.1117/12.2612453
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18839
dc.subjectOptical Coherence Tomography Applications
dc.subjectCerebrovascular and Carotid Artery Diseases
dc.subjectPhotoacoustic and Ultrasonic Imaging
dc.titleQuantitative scatterer density estimator to characterize tissue-based phantom in optical coherence tomography
dc.typeArticle

Files

Collections

All