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Ultrasound (US) imaging is widely employed for diagnosis and staging ofperipheral vascular diseases (PVD), mainly due to its high availability and thefact it does not emit radiation. However, high inter-operator variability and alack of repeatability of US image acquisition hinder the implementation ofextensive screening programs. To address this challenge, we propose anend-to-end workflow for automatic robotic US screening of tubular structuresusing only the real-time US imaging feedback. We first train a U-Net forreal-time segmentation of the vascular structure from cross-sectional USimages. Then, we represent the detected vascular structure as a 3D point cloudand use it to estimate the longitudinal axis of the target tubular structureand its mean radius by solving a constrained non-linear optimization problem.Iterating the previous processes, the US probe is automatically aligned to theorientation normal to the target tubular tissue and adjusted online to centerthe tracked tissue based on the spatial calibration. The real-time segmentationresult is evaluated both on a phantom and in-vivo on brachial arteries ofvolunteers. In addition, the whole process is validated both in simulation andphysical 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 screentubular 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.

Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback