Intensity-invariant scatterer density estimation for optical coherence tomography using deep convolutional neural network

Abstract

A convolutional neural networks (CNN) based scatterer density estimator for optical coherence tomography (OCT) is presented. In order to train the OCT, small patches of OCT speckle image were numerically generated. In this numerical image generation, the imaging parameters including the resolutions, probe power, signal-to-noise ratio, and scatterer density were randomly defined. So, the CNN was trained to estimate the imaging parameters from the generated OCT image patch. The results showed that our CNN estimator can estimate the parameters from the OCT speckle images.