  The first choice in diagnostic imaging for patients suffering from peripheral
arterial disease is 2D ultrasound (US). However, for a proper imaging process,
a skilled and experienced sonographer is required. Additionally, it is a highly
user-dependent operation. A robotized US system that autonomously scans the
peripheral arteries has the potential to overcome these limitations. In this
work, we extend a previously proposed system by a hierarchical image analysis
pipeline based on convolutional neural networks in order to control the robot.
The system was evaluated by checking its feasibility to keep the vessel lumen
of a leg phantom within the US image while scanning along the artery. In 100 %
of the images acquired during the scan process the whole vessel lumen was
visible. While defining an insensitivity margin of 2.74 mm, the mean absolute
distance between vessel center and the horizontal image center line was 2.47 mm
and 3.90 mm for an easy and complex scenario, respectively. In conclusion, this
system presents the basis for fully automatized peripheral artery imaging in
humans using a radiation-free approach.
