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Abstract #4155

Weakly-Supervised Carotid Vessel Wall Sub-pixel Segmentation Using Global-Local Context Aggregation and PolarMask

Jiaqi Dou1, Song Tian2, Yuze Li1, Ziming Xu1, Shuo Chen1, Yajie Wang1, and Huijun Chen1
1Center for Biomedical Imaging Research, Tsinghua University, Beijing, China, 2MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China


Carotid vessel wall segmentation on 3D black-blood MRI is a key step in plaque burden assessment and atherosclerotic lesions identification. In this study, a two-stage weakly-supervised carotid vessel wall segmentation approach was developed using limited manual delineations on 3D black-blood MR images. First, a global-local context aggregation strategy was used to identify bilateral carotid arteries robustly. Then, distances from artery center to boundaries were regressed in a polar coordinate by PolarMask to segment the vessel wall. The proposed segmentation approach outperformed Attention UNet (Quantitative score: 0.795±0.170 vs. 0.729±0.208) and showed great potential in quantitative atherosclerosis analysis.

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