  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.
