Conference proceeding
Automatic Segmentation of Calcified Plaque in Carotid Arteries
Proceedings (International Symposium on Biomedical Imaging), pp 1-5
14 Apr 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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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Details
- Title
- Automatic Segmentation of Calcified Plaque in Carotid Arteries
- Creators
- Jiehyun Kim - University of Massachusetts BostonKevin Wang - Net Health Systems Inc.,Boston,MA,United StatesYu Sakai - University of PennsylvaniaYouxiang Zhu - University of Massachusetts BostonAndrew C. Hu - University of PennsylvaniaHuy Q. Phi - Drexel UniversityNathan Arnett - Brigham and Women's HospitalGrace J. Wang - University of PennsylvaniaBrett L. Cucchiara - University of PennsylvaniaJae W. Song - University of PennsylvaniaDaniel Haehn - University of Massachusetts Boston
- Publication Details
- Proceedings (International Symposium on Biomedical Imaging), pp 1-5
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001546451000494
- Scopus ID
- 2-s2.0-105005828252
- Other Identifier
- 991022197421904721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Engineering, Biomedical
- Radiology, Nuclear Medicine & Medical Imaging