Quantitative scatterer density estimator to characterize tissue-based phantom in optical coherence tomography - Thitiya Seesan; Ibrahim G. Abd El-Sadek; Pradipta Mukherjee; Kensuke Oikawa; Prathan Buranasiri; Yoshiaki Yasuno (My University, 2022)
We 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.
Keywords: Optical Coherence Tomography Applications; Cerebrovascular and Carotid Artery Diseases; Photoacoustic and Ultrasonic Imaging