  Quantitative UltraSound (QUS) aims to reveal information about the tissue
microstructure using backscattered echo signals from clinical scanners. Among
different QUS parameters, scatterer number density is an important property
that can affect estimation of other QUS parameters. Scatterer number density
can be classified into high or low scatterer densities. If there are more than
10 scatterers inside the resolution cell, the envelope data is considered as
Fully Developed Speckle (FDS) and otherwise, as Under Developed Speckle (UDS).
In conventional methods, the envelope data is divided into small overlapping
windows (a strategy here we refer to as patching), and statistical parameters
such as SNR and skewness are employed to classify each patch of envelope data.
However, these parameters are system dependent meaning that their distribution
can change by the imaging settings and patch size. Therefore, reference
phantoms which have known scatterer number density are imaged with the same
imaging settings to mitigate system dependency. In this paper, we aim to
segment regions of ultrasound data without any patching. A large dataset is
generated which has different shapes of scatterer number density and mean
scatterer amplitude using a fast simulation method. We employ a convolutional
neural network (CNN) for the segmentation task and investigate the effect of
domain shift when the network is tested on different datasets with different
imaging settings. Nakagami parametric image is employed for the multi-task
learning to improve the performance. Furthermore, inspired by the reference
phantom methods in QUS, A domain adaptation stage is proposed which requires
only two frames of data from FDS and UDS classes. We evaluate our method for
different experimental phantoms and in vivo data.
