Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery
CMF-ELSeg provides high-precision segmentation of CMF structures and teeth by leveraging multiple models, serving as a practical tool for clinical applications and enhancing patient-specific treatment planning in CMF surgery.
Authors
- Jiahao Bao (ORCID: https://orcid.org/0000-0001-5034-1274)
- Mengjia Cheng
- Xinyu Xu (ORCID: https://orcid.org/0000-0002-1415-4452)
- Hongbo Yu (ORCID: https://orcid.org/0000-0001-8147-7246)
- Yaofeng Wen
- Jiayi Zhu (ORCID: https://orcid.org/0000-0003-3837-7631)
- Yong Kuen Ho (ORCID: https://orcid.org/0000-0003-2731-7094)
- Dahong Qian (ORCID: https://orcid.org/0000-0001-9715-6674)
- Weiyi Cui
- Yiming Wang (ORCID: https://orcid.org/0000-0003-3298-5134)
- Yang Yang (ORCID: https://orcid.org/0000-0001-8586-872X)
- Yifeng Sun
- Shunyao Shen
- Linfeng Fan
- Huazhen Liu
- C.-C. Ku
- Zongcai Tan
Institutions
- Shanghai Medical College of Fudan University (CN)
- Chinese University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Shanghai Ninth People's Hospital (CN)
- Shanghai Dianji University (CN)
- Imperial College London (GB)
- University of Hong Kong (HK)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-05-08
- DOI
- https://doi.org/10.3389/fbioe.2025.1580502
- Citations
- 7
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 12.53
Funders
- Natural Science Foundation of Shanghai
- National Natural Science Foundation of China