Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography

Abstract

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.