Autonomous Robotic Screening of Tubular Structures Based Only on Real-Time Ultrasound Imaging Feedback

Ultrasound (US) imaging is widely employed for diagnosis and staging of vascular diseases, mainly due to its high availability and the fact it does not emit ionizing radiation. However, high interoperator variability limits the repeatability of US image acquisition. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only real-time US imaging feedback. First, a U-Net was trained for real-time segmentation of vascular structure from cross-sectional US images. Then, we represented the detected vascular structure as a 3-D point cloud, which was used to estimate the centerline of the target structure and its local radius by solving a constrained nonlinear optimization problem. Iterating the previous processes, the US probe was automatically aligned to the normal direction of the target structure, while the object was constantly maintained in the center of the US view. The real-time segmentation result was evaluated both on a phantom and
in vivo
on brachial arteries of volunteers. In addition, the whole process was validated using both simulation and physical phantoms. The mean absolute orientation, centering, and radius error (
$\pm$
SD) on a gel phantom were
$\text{3.7}\pm \text{1.6}^{\circ }$
,
$0.2\pm \text{0.2}\,\text{mm}$
and
$0.8\pm \text{0.4}\,\text{mm}$
, respectively. The results indicate that the method can automatically screen tubular structures with an optimal probe orientation (i.e., normal to the vessel) and accurately estimate the radius of the target structure.