  Quantitative ultrasound (QUS) aims to find properties of scatterers which are
related to the tissue microstructure. Among different QUS parameters, scatterer
number density has been found to be a reliable biomarker for detecting
different abnormalities. The homodyned K-distribution (HK-distribution) is a
model for the probability density function of the ultrasound echo amplitude
that can model different scattering scenarios but requires a large number of
samples to be estimated reliably. Parametric images of HK-distribution
parameters can be formed by dividing the envelope data into small overlapping
patches and estimating parameters within the patches independently. This
approach imposes two limiting constraints, the HK-distribution parameters are
assumed to be constant within each patch, and each patch requires enough
independent samples. In order to mitigate those problems, we employ a deep
learning approach to estimate parametric images of scatterer number density
(related to HK-distribution shape parameter) without patching. Furthermore, an
uncertainty map of the network's prediction is quantified to provide insight
about the confidence of the network about the estimated HK parameter values.
