  Ultrasound (US) imaging is widely employed for diagnosis and staging of
peripheral vascular diseases (PVD), mainly due to its high availability and the
fact it does not emit radiation. However, high inter-operator variability and a
lack of repeatability of US image acquisition hinder the implementation of
extensive screening programs. To address this challenge, we propose an
end-to-end workflow for automatic robotic US screening of tubular structures
using only the real-time US imaging feedback. We first train a U-Net for
real-time segmentation of the vascular structure from cross-sectional US
images. Then, we represent the detected vascular structure as a 3D point cloud
and use it to estimate the longitudinal axis of the target tubular structure
and its mean radius by solving a constrained non-linear optimization problem.
Iterating the previous processes, the US probe is automatically aligned to the
orientation normal to the target tubular tissue and adjusted online to center
the tracked tissue based on the spatial calibration. The real-time segmentation
result is evaluated both on a phantom and in-vivo on brachial arteries of
volunteers. In addition, the whole process is validated both in simulation and
physical phantoms. The mean absolute radius error and orientation error ($\pm$
SD) in the simulation are $1.16\pm0.1~mm$ and $2.7\pm3.3^{\circ}$,
respectively. On a gel phantom, these errors are $1.95\pm2.02~mm$ and
$3.3\pm2.4^{\circ}$. This shows that the method is able to automatically screen
tubular tissues with an optimal probe orientation (i.e. normal to the vessel)
and at the same to accurately estimate the mean radius, both in real-time.
