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Automatic Segmentation of Calcified Plaque in Carotid Arteries
Conference proceeding

Automatic Segmentation of Calcified Plaque in Carotid Arteries

Jiehyun Kim, Kevin Wang, Yu Sakai, Youxiang Zhu, Andrew C. Hu, Huy Q. Phi, Nathan Arnett, Grace J. Wang, Brett L. Cucchiara, Jae W. Song, …
Proceedings (International Symposium on Biomedical Imaging), pp 1-5
14 Apr 2025
DOI: https://doi.org/10.1109/ISBI60581.2025.10981168

Abstract

Biological system modeling Biomedical imaging Calcified Plaque Carotid arteries Carotid Artery CTA Manuals Reproducibility of results Seg-mentation Software development management Solid modeling Stroke (medical condition) Three-dimensional displays UNet Stroke
Stroke remains a leading cause of death globally, with calcified plaque in the carotid artery being a significant risk factor. Evaluating the impact of calcified carotid plaque, particularly in patients with embolic stroke of undetermined source (ESUS), is critical yet challenging. Manual segmentation, essential for assessing stroke risk, is time-consuming, and conventional methods like 2D and 3D UNet often struggle with the small size of calcified plaques. Therefore, this paper in-troduces a two-step segmentation process. First, segments the carotid artery to narrow the search space and focus on the region of interest around the artery. Then, it segments the calcified plaque within that targeted region. This approach achieves an intersection over union (IoU) of 0.9412 for the 2D model and 0.8095 for the 3D model, outperforming the baseline methods that directly segment plaques. All developments are open source and publicly accessible on our GitHub: https://github.com/mpsych/CACTAS-AI.

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