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).

翻译：医学超声（US）凭借其无辐射优势，被广泛用于评估和分期血管疾病，尤其是初步筛查项目。然而，从横截面超声图像中自动分割细小管状结构（如尺动脉）仍具挑战性。为解决该问题，本文提出DopUS-Net和血管重识别模块，利用多普勒效应增强最终分割结果。首先，DopUS-Net将多普勒图像与B模式图像相结合，以提高小血管的分割精度和鲁棒性。它集成两个编码器以充分挖掘多普勒信号的潜力，并引入循环神经网络模块保留序列信息。第一个编码器输入为双通道双模图像，代表灰阶多普勒与B模式图像的融合，以确保解剖空间正确性；第二个编码器作用于纯多普勒图像，提供区域建议。其次，得益于多普勒信号，本文首次引入在线动脉重识别模块，实时定性评估分割结果并自动优化探头姿态以增强多普勒图像。该质量感知模块能够实现机器人筛查的闭环控制，进一步提高图像分割的置信度和鲁棒性。实验结果表明，结合重识别过程的所提方法可显著提升分割结果的精度和鲁棒性（Dice系数：从0.54提高到0.86；交并比：从0.47提高到0.78）。