Automated Sella-Turcica Annotation and Mesh Alignment of 3D Stereophotographs for Craniosynostosis Patients Using a PCA-FFNN Based Approach
These findings highlight the potential of the PCA-FFNN approach to provide a more reliable, noninvasive solution for cranial assessment, improving craniosynostosis follow-up and enhancing clinical outcomes.
Authors
- Thomas J. J. Maal
- John van Aalst
- Freek Bielevelt (ORCID: https://orcid.org/0000-0003-2539-562X)
- Hans Delye (ORCID: https://orcid.org/0000-0001-5278-3148)
- Guido de Jong (ORCID: https://orcid.org/0000-0002-8014-0622)
- Najiba Chargi (ORCID: https://orcid.org/0009-0001-4008-6721)
- Marloes Nienhuijs
Institutions
- Radboud University Nijmegen (NL)
- University Medical Center (US)
- Radboud University Medical Center (NL)
- AZ Turnhout (BE)
Publication Details
- Journal
- Journal of Craniofacial Surgery
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1097/scs.0000000000011623
- Citations
- 1
- Primary Topic
- Craniofacial Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.82