In nuclear medicine, radioiodine therapy is prescribed to treat diseases like hyperthyroidism. The calculation of the prescribed dose depends, amongst other factors, on the thyroid volume. This is currently estimated using conventional 2D ultrasound imaging. However, this modality is inherently user-dependant, resulting in high variability in volume estimations. To increase reproducibility and consistency, we uniquely combine a neural network-based segmentation with an automatic robotic ultrasound scanning for thyroid volumetry. The robotic acquisition is achieved by using a 6 DOF robotic arm with an attached ultrasound probe. Its movement is based on an online segmentation of each thyroid lobe and the appearance of the US image. During post-processing, the US images are segmented to obtain a volume estimation. In an ablation study, we demonstrated the superiority of the motion guidance algorithms for the robot arm movement compared to a naive linear motion, executed by the robot in terms of volumetric accuracy. In a user study on a phantom, we compared conventional 2D ultrasound measurements with our robotic system. The mean volume measurement error of ultrasound expert users could be significantly decreased from 20.85+/-16.10% to only 8.23+/-3.10% compared to the ground truth. This tendency was observed even more in non-expert users where the mean error improvement with the robotic system was measured to be as high as $85\%$ which clearly shows the advantages of the robotic support.
翻译:在核医学中,放射碘疗法是用来治疗超机器人病等疾病的。除其他因素外,处方剂量的计算取决于甲状腺体积。目前使用常规的 2D 超声成像进行估算。然而,这一模式本质上是用户依赖的,导致体积估计的差别很大。为了提高再生性和一致性,我们将神经网络分割与甲状腺体积的自动机器人超声波扫描结合了起来。机器人的获取是通过使用6 DOF机器人臂和随附的超声波探测器来实现的。其运动基于每个甲状腺的在线分割和美国图像的外观。在后处理期间,美国图像被分割成一个部分,以获得体积估计。在一项减缩研究中,我们展示了机器人手臂运动指导算法优于天线性运动。在一项用户的体积精度精确度研究中,我们将常规的2D超声波测量法与我们的机器人系统比较。超声波测量误差的体积误差为20.85美元,甚至比专家用户的直径差为8.10+平比的直径偏差,从20.