DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler
  Signal
Zhongliang Jiang Felix Duelmer Nassir Navab
Medical ultrasound (US) is widely used to evaluate and stage vascular
diseases, in particular for the preliminary screening program, due to the
advantage of being radiation-free. However, automatic segmentation of small
tubular structures (e.g., the ulnar artery) from cross-sectional US images is
still challenging. To address this challenge, this paper proposes the DopUS-Net
and a vessel re-identification module that leverage the Doppler effect to
enhance the final segmentation result. Firstly, the DopUS-Net combines the
Doppler images with B-mode images to increase the segmentation accuracy and
robustness of small blood vessels. It incorporates two encoders to exploit the
maximum potential of the Doppler signal and recurrent neural network modules to
preserve sequential information. Input to the first encoder is a two-channel
duplex image representing the combination of the grey-scale Doppler and B-mode
images to ensure anatomical spatial correctness. The second encoder operates on
the pure Doppler images to provide a region proposal. Secondly, benefiting from
the Doppler signal, this work first introduces an online artery
re-identification module to qualitatively evaluate the real-time segmentation
results and automatically optimize the probe pose for enhanced Doppler images.
This quality-aware module enables the closed-loop control of robotic screening
to further improve the confidence and robustness of image segmentation. The
experimental results demonstrate that the proposed approach with the
re-identification process can significantly improve the accuracy and robustness
of the segmentation results (dice score: from 0:54 to 0:86; intersection over
union: from 0:47 to 0:78).