2D US scans of the neck region for an exemplary volunteer.

a) Width and depth of right thyroid lobe marked, b) Length of right thyroid lobe marked.</p

Mean and standard deviation (mean±SD) in milliliters (<i>ml</i>) of the interobserver variability of two MDs in 2D and 3D for both groups.

Significant different p-values are marked in bold.</p

Fig 1 -

T1 VIBE MRI of the neck region for an exemplary volunteer, a) axial view, b) sagittal view, c) coronal view.</p

Bland-Altman plots for the interobserver variability between sets of two medical doctors from Group 2.

All values are given in ml. Row 1, 2 and 3 show results between MD 1 and MD 2, MD 1 and MD 3, and MD 2 and MD 3, respectively. The columns show the results for 2D (left) and 3D measurements (right).</p

Mean and standard deviation (mean±SD) in percent (%) of the intraobserver variability for each MD in 2D and 3D for both groups (Gr. 1 and 2).

The last column shows whether a significant difference exists between the averaged 2D and 3D volume estimation sets, being the significant different p-values in bold.</p

Illustration of the QuickNAT architecture used here for automatically segmenting thyroid in 3D US volumes.

Illustration of the QuickNAT architecture used here for automatically segmenting thyroid in 3D US volumes.</p

Bland-Altman plots for the intraobserver variability of each medical doctor from Group 2.

All values are given in ml. Row 1, 2 and 3 show results for MD 1, 2, and 3, respectively. The columns show the results for 2D US (left) and 3D US measurements (right).</p

Mean and standard deviation (mean±SD) of the volume of the first scan series for each MD in 2D and 3D in milliliters (<i>ml</i>).

Statistical significance with respect to the MRI volume is reported, being statistical significant p-values in bold.</p

Fig 5 -

Ultrasound image (left), segmentation (middle) and overlay (right) of a good segmentation (first row), a segmentation which segmented too many voxels of the isthmus (second row), a segmentation with too few voxels of the isthmus segmented (third row) and a segmentation with a segmented nodule (fourth row).</p

Schematic representation of the automatic segmentation workflow.

The raw 3D scans of each lobe are pre-processed by rotation into axial view and resizing as well as centering. Then 2D images are extracted which serve as input to the network. The output of the network is a segmentation from which the volume is calculated by multiplication of pixels which include a segmentation with the pixel volume.</p