Intracranial Vessel Wall Segmentation For Atherosclerotic Plaque Quantification.

Intracranial vessel wall segmentation is critical for the quantitative assessment of intracranial atherosclerosis based on magnetic resonance vessel wall imaging. This work further improves on a previous 2D deep learning segmentation network by the utilization of 1) a 2.5D structure to balance network complexity and regularizing geometry continuity; 2) a UNET++ model to achieve structure adaptation; 3) an additional approximated Hausdorff distance (HD) loss into the objective to enhance geometry conformality; and 4) landing in a commonly used morphological measure of plaque burden - the normalized wall index (NWI) - to match the clinical endpoint. The modified network achieved Dice similarity coefficient of $0.9172 \pm 0.0598$ and $0.7833 \pm 0.0867$, HD of $0.3252 \pm 0.5071$ mm and $0.4914 \pm 0.5743$ mm, mean surface distance of $0.0940 \pm 0.0781$ mm and $0.1408 \pm 0.0917$ mm for the lumen and vessel wall, respectively. These results compare favorably to those obtained by the original 2D UNET on all segmentation metrics. Additionally, the proposed segmentation network reduced the mean absolute error in NWI from $0.0732 \pm 0.0294$ to $0.0725 \pm 0.0333$.