  Quantitative Ultrasound (QUS) provides important information about the tissue
properties. QUS parametric image can be formed by dividing the envelope data
into small overlapping patches and computing different speckle statistics such
as parameters of the Nakagami and Homodyned K-distributions (HK-distribution).
The calculated QUS parametric images can be erroneous since only a few
independent samples are available inside the patches. Another challenge is that
the envelope samples inside the patch are assumed to come from the same
distribution, an assumption that is often violated given that the tissue is
usually not homogenous. In this paper, we propose a method based on
Convolutional Neural Networks (CNN) to estimate QUS parametric images without
patching. We construct a large dataset sampled from the HK-distribution, having
regions with random shapes and QUS parameter values. We then use a well-known
network to estimate QUS parameters in a multi-task learning fashion. Our
results confirm that the proposed method is able to reduce errors and improve
border definition in QUS parametric images.
