An open deep learning-based framework and model for tooth instance segmentation in dental CBCT
OralSeg can offer researchers and clinicians a user-friendly tool for tooth-level instance segmentation, which may assist in clinical diagnosis, educational training, and research, and contribute to the broader adoption of digital dentistry in precision medicine.
Authors
- Andrew Nalley (ORCID: https://orcid.org/0000-0001-7594-1276)
- Mihai Tarce (ORCID: https://orcid.org/0000-0001-6466-5261)
- Basel Khalil (ORCID: https://orcid.org/0009-0002-8072-7287)
- Yan Xu (ORCID: https://orcid.org/0009-0007-8187-0276)
- You Zhou (ORCID: https://orcid.org/0009-0006-4218-9712)
Institutions
- Prince Philip Dental Hospital (CN)
- Nanjing Drum Tower Hospital (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Clinical Oral Investigations
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1007/s00784-025-06578-w
- Citations
- 4
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 7.16