  Deep Neural Networks (DNNs) have become ubiquitous in medical image
processing and analysis. Among them, U-Nets are very popular in various image
segmentation tasks. Yet, little is known about how information flows through
these networks and whether they are indeed properly designed for the tasks they
are being proposed for. In this paper, we employ information-theoretic tools in
order to gain insight into information flow through U-Nets. In particular, we
show how mutual information between input/output and an intermediate layer can
be a useful tool to understand information flow through various portions of a
U-Net, assess its architectural efficiency, and even propose more efficient
designs.
