  Objective: Vessel-wall-volume (VWV) and localized vessel-wall-thickness (VWT)
measured from 3D ultrasound (US) carotid images are sensitive to
anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT
measurements require the lumen-intima (LIB) and media-adventitia boundaries
(MAB) at the common and internal carotid arteries (CCA and ICA). However, most
existing segmentation techniques were capable of automating only CCA
segmentation. An approach capable of segmenting the MAB and LIB from the CCA
and ICA was required to accelerate VWV and VWT quantification. Methods:
Segmentation for CCA and ICA were performed independently using the proposed
two-channel U-Net, which was driven by a novel loss function known as the
adaptive triple Dice loss (ADTL). A test-time augmentation (TTA) approach is
used, in which segmentation was performed three times based on axial images and
its flipped versions; the final segmentation was generated by pixel-wise
majority voting. Results: Experiments involving 224 3DUS volumes produce a
Dice-similarity-coefficient (DSC) of 95.1%$\pm$4.1% and 91.6%$\pm$6.6% for the
MAB and LIB, in the CCA, respectively, and 94.2%$\pm$3.3% and 89.0%$\pm$8.1%
for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently
contributed to a statistically significant improvement to all boundaries except
the LIB in ICA. The total time required to segment the entire 3DUS volume
(CCA+ICA) is 1.4s. Conclusion: The proposed two-channel U-Net with ADTL and TTA
can segment the CCA and ICA accurately and efficiently from the 3DUS volume.
Significance: Our approach has the potential to accelerate the transition of
3DUS measurements of carotid atherosclerosis to clinical research.
