Automatic vessel segmentation and reformation of non-contrast coronary magnetic resonance angiography using transfer learning-based three-dimensional U-net with attention mechanism
The AI auto-segmentation system can effectively facilitate CMRA vessel reformation and reduce the time consumption for radiologists. It has the potential to become a standard component of daily workflows, optimizing the clinical application of CMRA in the future.
Authors
- Xinsheng Mao
- Yanyu Li (ORCID: https://orcid.org/0000-0003-1240-4785)
- Jian Cao (ORCID: https://orcid.org/0000-0001-7934-5025)
- Yueting Xiao
- Jian Wang (ORCID: https://orcid.org/0000-0002-6261-7829)
- Chao Zheng (ORCID: https://orcid.org/0000-0001-8454-5988)
- Yining Wang (ORCID: https://orcid.org/0000-0003-3193-2487)
- Lin Lü (ORCID: https://orcid.org/0000-0001-7743-7022)
- Difei Jiang
- Yijia Zheng (ORCID: https://orcid.org/0000-0002-6585-8273)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of Cardiovascular Magnetic Resonance
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1016/j.jocmr.2024.101126
- Citations
- 3
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- National Natural Science Foundation of China
- Peking Union Medical College Hospital