DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal

TL;DR

DopUS-Net enhances robotic ultrasound imaging by integrating Doppler signals with deep learning to improve segmentation accuracy of small blood vessels, enabling real-time quality assessment and probe optimization.

Contribution

This work introduces DopUS-Net with a vessel re-identification module that leverages Doppler signals for improved segmentation and real-time quality control in robotic ultrasound imaging.

Findings

Segmentation accuracy improved (Dice score: 0.54 to 0.86)

Enhanced robustness of small vessel segmentation

Effective real-time probe pose optimization

Abstract

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).