Physics-based data augmentation for improved training of cone-beam computed tomography auto-segmentation of the female pelvis
This study demonstrates the successful training of auto-segmentation model for female pelvic CBCT images, without necessarily delineating CBCTs manually.
Authors
- Rob H.N. Tijssen (ORCID: https://orcid.org/0000-0003-1355-7150)
- Yvonne J M de Hond (ORCID: https://orcid.org/0000-0003-4755-672X)
- An-Sofie Verrijssen (ORCID: https://orcid.org/0000-0002-5193-2831)
- Coen Hurkmans (ORCID: https://orcid.org/0000-0003-2268-0492)
- P. M. A. van Haaren
Institutions
- Radboud University Nijmegen (NL)
- Catharina Ziekenhuis (NL)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1016/j.phro.2025.100744
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00