  Background: Thyroid volumetry is crucial in diagnosis, treatment and
monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D
ultrasound is highly operator-dependent. This study compares 2D ultrasound and
tracked 3D ultrasound with an automatic thyroid segmentation based on a deep
neural network regarding inter- and intraobserver variability, time and
accuracy. Volume reference was MRI. Methods: 28 healthy volunteers were scanned
with 2D and 3D ultrasound as well as by MRI. Three physicians (MD 1, 2, 3) with
different levels of experience (6, 4 and 1 a) performed three 2D ultrasound and
three tracked 3D ultrasound scans on each volunteer. In the 2D scans the
thyroid lobe volumes were calculated with the ellipsoid formula. A
convolutional deep neural network (CNN) segmented the 3D thyroid lobes
automatically. On MRI (T1 VIBE sequence) the thyroid was manually segmented by
an experienced medical doctor. Results: The CNN was trained to obtain a dice
score of 0.94. The interobserver variability comparing two MDs showed mean
differences for 2D and 3D respectively of 0.58 ml to 0.52 ml (MD1 vs. 2), -1.33
ml to -0.17 ml (MD1 vs. 3) and -1.89 ml to -0.70 ml (MD2 vs. 3). Paired samples
t-tests showed significant differences in two comparisons for 2D and none for
3D. Intraobsever variability was similar for 2D and 3D ultrasound. Comparison
of ultrasound volumes and MRI volumes by paired samples t-tests showed a
significant difference for the 2D volumetry of all MDs, and no significant
difference for 3D ultrasound. Acquisition time was significantly shorter for 3D
ultrasound. Conclusion: Tracked 3D ultrasound combined with a CNN segmentation
significantly reduces interobserver variability in thyroid volumetry and
increases the accuracy of the measurements with shorter acquisition times.
