Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study
The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.
Authors
- Kento Odaka (ORCID: https://orcid.org/0000-0002-4717-533X)
- Florian Kernen (ORCID: https://orcid.org/0000-0002-3067-0756)
- Stefaan Bergé
- Bram van Ginneken (ORCID: https://orcid.org/0000-0003-2028-8972)
- Ute Ludwig (ORCID: https://orcid.org/0000-0002-0526-5935)
- Tong Xi (ORCID: https://orcid.org/0000-0002-9142-9253)
- Tabea Flügge (ORCID: https://orcid.org/0000-0002-1288-8452)
- Niels van Nistelrooij
- Shankeeth Vinayahalingam (ORCID: https://orcid.org/0000-0002-2679-3841)
- Max Heiland (ORCID: https://orcid.org/0000-0002-4987-2913)
- S. Kellner
Institutions
- Radboud University Nijmegen (NL)
- Tokyo Dental College (JP)
- University of Freiburg (DE)
- University Medical Center Freiburg (DE)
- University Medical Center (US)
- Radboud University Medical Center (NL)
- Philips (United Kingdom) (GB)
- Humboldt-Universität zu Berlin (DE)
- University Hospital Münster (DE)
- University Hospital and Clinics (US)
- Klinik für Schlafmedizin (CH)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Dentomaxillofacial Radiology
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1093/dmfr/twae059
- Citations
- 5
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.47