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Background: Thyroid volumetry is crucial in diagnosis, treatment andmonitoring of thyroid diseases. However, conventional thyroid volumetry with 2Dultrasound is highly operator-dependent. This study compares 2D ultrasound andtracked 3D ultrasound with an automatic thyroid segmentation based on a deepneural network regarding inter- and intraobserver variability, time andaccuracy. Volume reference was MRI. Methods: 28 healthy volunteers were scannedwith 2D and 3D ultrasound as well as by MRI. Three physicians (MD 1, 2, 3) withdifferent levels of experience (6, 4 and 1 a) performed three 2D ultrasound andthree tracked 3D ultrasound scans on each volunteer. In the 2D scans thethyroid lobe volumes were calculated with the ellipsoid formula. Aconvolutional deep neural network (CNN) segmented the 3D thyroid lobesautomatically. On MRI (T1 VIBE sequence) the thyroid was manually segmented byan experienced medical doctor. Results: The CNN was trained to obtain a dicescore of 0.94. The interobserver variability comparing two MDs showed meandifferences for 2D and 3D respectively of 0.58 ml to 0.52 ml (MD1 vs. 2), -1.33ml to -0.17 ml (MD1 vs. 3) and -1.89 ml to -0.70 ml (MD2 vs. 3). Paired samplest-tests showed significant differences in two comparisons for 2D and none for3D. Intraobsever variability was similar for 2D and 3D ultrasound. Comparisonof ultrasound volumes and MRI volumes by paired samples t-tests showed asignificant difference for the 2D volumetry of all MDs, and no significantdifference for 3D ultrasound. Acquisition time was significantly shorter for 3Dultrasound. Conclusion: Tracked 3D ultrasound combined with a CNN segmentationsignificantly reduces interobserver variability in thyroid volumetry andincreases the accuracy of the measurements with shorter acquisition times.

Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry