Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
A multi-organ segmentation model was developed, showcasing enhanced robustness when trained on combined datasets. This model is suitable for various OARs and can be applied to multiple datasets in clinical settings.
Authors
- Matteo Maspero (ORCID: https://orcid.org/0000-0003-0347-3375)
- Max M. van Noesel (ORCID: https://orcid.org/0000-0003-0503-5838)
- Marry M. van den Heuvel‐Eibrink (ORCID: https://orcid.org/0000-0002-7760-879X)
- Mianyong Ding (ORCID: https://orcid.org/0009-0000-4642-3138)
- Annemieke Simone Littooij (ORCID: https://orcid.org/0000-0002-1839-0882)
- Raquel Dávila Fajardo (ORCID: https://orcid.org/0000-0002-4729-825X)
- Martine van Grotel (ORCID: https://orcid.org/0000-0002-8849-8573)
- Geert O. Janssens (ORCID: https://orcid.org/0000-0002-0331-713X)
Institutions
- Utrecht University (NL)
- University Medical Center Utrecht (NL)
- Princess Máxima Center (NL)
- Wilhelmina Children's Hospital (NL)
Publication Details
- Journal
- Radiotherapy and Oncology
- Published
- 2025-05-04
- DOI
- https://doi.org/10.1016/j.radonc.2025.110914
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.77