Multi-stage deep learning architecture for carotid artery segmentation and stenosis evaluation: comparative study with DSA
Thus, the proposed architecture achieved accuracy comparable to manual segmentation by physicians and demonstrated high consistency with DSA diagnostic criteria. By shortening diagnostic time and minimizing inter-observer variability, the proposed architecture is promising to offer a reliable, efficient, and intelligent tool for diagnosing head and neck atherosclerotic disease and assessing stroke risk.
Authors
- Kangyi Pan
- Zhiji Zheng
- Kun Zhou (ORCID: https://orcid.org/0000-0003-0650-9521)
- Zhimeng Cui (ORCID: https://orcid.org/0009-0007-6970-1402)
- Hui Fang (ORCID: https://orcid.org/0009-0006-8619-3043)
- Daoying Geng (ORCID: https://orcid.org/0000-0002-1707-1521)
- Xiao Luo
- Qiqi Lu (ORCID: https://orcid.org/0000-0002-6293-0073)
- Wanchen Liu
- Xiao Liu
- Xin Cao
Institutions
- Fudan University (CN)
- Ningbo First Hospital (CN)
Publication Details
- Journal
- Journal of Cardiovascular Magnetic Resonance
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1016/j.jocmr.2026.102683
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00