Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss.

Purpose: Vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT) measured from three-dimensional (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 segmenting the CCA only. 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 was 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) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test-time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input 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% ± 4.1% and 91.6% ± 6.6% for the MAB and LIB, in the CCA, respectively, and 94.2% ± 3.3% and 89.0% ± 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.

Conclusions: The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.