Enhancing patient-specific deep learning based segmentation for abdominal magnetic resonance imaging-guided radiation therapy: A framework conditioned on prior segmentation
The proposed workflow was able to generate accurate patient-specific segmentations while avoiding training one model per patient and allowing for a seamless integration into clinical practice.
Authors
- Stefanie Corradini (ORCID: https://orcid.org/0000-0001-8709-7252)
- Thomas Wendler (ORCID: https://orcid.org/0000-0002-0915-0510)
- Claus Belka (ORCID: https://orcid.org/0000-0002-1287-7825)
- Shadi Albarqouni (ORCID: https://orcid.org/0000-0003-2157-2211)
- Nassir Navab (ORCID: https://orcid.org/0000-0002-6032-5611)
- Christopher Kurz
- Guillaume Landry (ORCID: https://orcid.org/0000-0003-1707-4068)
- Francesca De Benetti (ORCID: https://orcid.org/0000-0002-5255-4553)
- Nikolaos Delopoulos
Institutions
- Helmholtz Association of German Research Centres (DE)
- University Hospital Bonn (DE)
- University Hospital Augsburg (DE)
- Helmholtz Zentrum München (DE)
- LMU Klinikum (DE)
- Helmholtz Institute Mainz (DE)
- German Center for Infection Research (DE)
- Centre de Coopération Universitaire Franco-Bavarois (DE)
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.phro.2025.100766
- Citations
- 4
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 7.53
Funders
- Deutsche Forschungsgemeinschaft