Ultrasound (US) imaging is widely used for diagnosing and monitoring arterial
diseases, mainly due to the advantages of being non-invasive, radiation-free,
and real-time. In order to provide additional information to assist clinicians
in diagnosis, the tubular structures are often segmented from US images. To
improve the artery segmentation accuracy and stability during scans, this work
presents a novel pulsation-assisted segmentation neural network (PAS-NN) by
explicitly taking advantage of the cardiac-induced motions. Motion
magnification techniques are employed to amplify the subtle motion within the
frequency band of interest to extract the pulsation signals from sequential US
images. The extracted real-time pulsation information can help to locate the
arteries on cross-section US images; therefore, we explicitly integrated the
pulsation into the proposed PAS-NN as attention guidance. Notably, a robotic
arm is necessary to provide stable movement during US imaging since magnifying
the target motions from the US images captured along a scan path is not
manually feasible due to the hand tremor. To validate the proposed robotic US
system for imaging arteries, experiments are carried out on volunteers' carotid
and radial arteries. The results demonstrated that the PAS-NN could achieve
comparable results as state-of-the-art on carotid and can effectively improve
the segmentation performance for small vessels (radial artery).

提出了一种新颖的脉动辅助分割神经网络 (PAS-NN)，通过明确利用心脏诱导的动作，运用运动放大技术来放大兴趣频带内的微小运动从而提取连续超声图像中的脉动信号，提高血管分割的准确性和稳定性，并通过创新性地整合脉动作为注意力指导，有效改善超声图像中小血管的分割性能。