Document Type
Article
Publication Date
2025
Keywords
Artificial Intelligence
Disciplines
Artificial Intelligence and Robotics
Abstract
Manual segmentation of calcified plaque, essential for assessing stroke risk, is time-consuming, and conventional methods like 2D and 3D UNet often struggle with the small size. We developed CACTAS-AI, a two-step segmentation process. This approach outperforms baseline methods in plaque segmentation.
Community Engaged/Serving
Part of the UMass Boston Community-Engaged Teaching, Research, and Service Series. //scholarworks.umb.edu/engage
Recommended Citation
Kim, Jiehyun; Wang, Kevin; Sakai, Yu; Zhu, Youxiang; Hu, Andrew C.; Phi, Huy Q.; Arnett, Nathan; Wang, Grace J.; Cucchiara, Brett L.; Song, Jae W.; and Haehn, Daniel, "SYMP25S: CACTAS-AI: Automatic Segmentaion of Calcified Plaque in Carotid Arteries" (2025). Paul English Applied Artificial Intelligence (AI) Institute Publications. 18.
https://scholarworks.umb.edu/ai_pubs/18
Comments
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