  We demonstrate a deep-learning-based scatterer density estimator (SDE) that
processes local speckle patterns of optical coherence tomography (OCT) images
and estimates the scatterer density behind each speckle pattern. The SDE is
trained using large quantities of numerically simulated OCT images and their
associated scatterer densities. The numerical simulation uses a noise model
that incorporates the spatial properties of three types of noise, i.e., shot
noise, relative-intensity noise, and non-optical noise. The SDE's performance
was evaluated numerically and experimentally using two types of scattering
phantom and in vitro tumor spheroids. The results confirmed that the SDE
estimates scatterer densities accurately. The estimation accuracy improved
significantly when compared with our previous deep-learning-based SDE, which
was trained using numerical speckle patterns generated from a noise model that
did not account for the spatial properties of noise.
