Open AccessDeep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography
Author(s) -
Thitiya Seesan,
Ibrahim Abd El-Sadek,
Pradipta Mukherjee,
Lida Zhu,
Kensuke Oikawa,
Akiyoshi Miyazawa,
Larina TzuWei Shen,
Satoshi Matsusaka,
Prathan Buranasiri,
Shuichi Makita,
Yoshiaki Yasuno
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.443343
Subject(s) - optical coherence tomography , estimator , optics , convolutional neural network , speckle pattern , physics , tomography , image resolution , scattering , mean squared error , mathematics , computer science , artificial intelligence , statistics
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 in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.