Deep learning-based key point detection algorithm assisting vessel centerline extraction
The proposed algorithm was able to automatically and accurately detect the 32 key points, especially those in the internal carotid artery and middle cerebral artery, improving vessel centerline extraction accuracy, and thus assisting in plaque assessment.
Authors
- Yang Long (ORCID: https://orcid.org/0000-0002-2445-6112)
- Zhang Shi (ORCID: https://orcid.org/0000-0001-7739-7497)
- Juan Pan
- Fei Feng (ORCID: https://orcid.org/0000-0002-3935-5146)
- Xiong Yang
- Xin Liu (ORCID: https://orcid.org/0000-0002-1756-887X)
- Xiqian Zhang
- Guanxun Cheng (ORCID: https://orcid.org/0000-0001-9168-1670)
- Hui Zhang (ORCID: https://orcid.org/0000-0002-4819-6482)
- Chengcheng Zhu (ORCID: https://orcid.org/0000-0001-6898-549X)
- Jia Gu
- W. Y. Sun (ORCID: https://orcid.org/0009-0007-1010-6318)
- Y. J. Mao (ORCID: https://orcid.org/0000-0002-8256-5923)
- Na Zhang (ORCID: https://orcid.org/0009-0002-7158-7825)
Institutions
- Sun Yat-sen University (CN)
- Chinese Academy of Sciences (CN)
- University of Washington (US)
- Fudan University (CN)
- Academy of Mathematics and Systems Science (CN)
- Zhongshan Hospital (CN)
- Peking University Shenzhen Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- University of Chinese Academy of Sciences (CN)
- City University of Macau (MO)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-21
- DOI
- https://doi.org/10.21037/qims-24-1949
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36