  Ultrasonic imaging algorithms used in many clinical and industrial
applications consist of three steps: A data pre-processing, an image formation
and an image post-processing step. For efficiency, image formation often relies
on an approximation of the underlying wave physics. A prominent example is the
Delay-And-Sum (DAS) algorithm used in reflectivity-based ultrasonic imaging.
Recently, deep neural networks (DNNs) are being used for the data
pre-processing and the image post-processing steps separately. In this work, we
propose a novel deep learning architecture that integrates all three steps to
enable end-to-end training. We examine turning the DAS image formation method
into a network layer that connects data pre-processing layers with image
post-processing layers that perform segmentation. We demonstrate that this
integrated approach clearly outperforms sequential approaches that are trained
separately. While network training and evaluation is performed only on
simulated data, we also showcase the potential of our approach on real data
from a non-destructive testing scenario.
