Personalized deep learning auto‐segmentation models for adaptive fractionated magnetic resonance‐guided radiation therapy of the abdomen
Personalized auto-segmentation models outperformed the population BMs. In most cases, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mtext>PS</mml:mtext> <mml:mi>BM</mml:mi></mml:msub> <mml:annotation>$\text{PS}_{\mathrm{BM}}$</mml:annotation></mml:semantics> </mml:math> delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionated treatment.
Authors
- Stefanie Corradini (ORCID: https://orcid.org/0000-0001-8709-7252)
- Sebastian Marschner (ORCID: https://orcid.org/0000-0002-8121-358X)
- Claus Belka (ORCID: https://orcid.org/0000-0002-1287-7825)
- Maria Kawula (ORCID: https://orcid.org/0000-0002-5039-8278)
- Christopher Kurz
- Guillaume Landry (ORCID: https://orcid.org/0000-0003-1707-4068)
- Chengtao Wei
- Marvin F. Ribeiro
Institutions
- LMU Klinikum (DE)
- Deutsches Konsortium für Translationale Krebsforschung (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1002/mp.17580
- Citations
- 7
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.25
Funders
- Wilhelm Sander-Stiftung