Leveraging network uncertainty to identify regions in rectal cancer clinical target volume auto-segmentations likely requiring manual edits
Our results validate the hypothesis that epistemic uncertainty estimates are a valuable tool to capture regions likely requiring clinically relevant edits.
Authors
- Peter M. A. van Ooijen (ORCID: https://orcid.org/0000-0002-8995-1210)
- Tomas Janssen (ORCID: https://orcid.org/0000-0003-2807-8670)
- Charlotte L. Brouwer (ORCID: https://orcid.org/0000-0002-2349-7369)
- Jan‐Jakob Sonke (ORCID: https://orcid.org/0000-0001-5155-5274)
- Federica Carmen Maruccio
- Rita Simões (ORCID: https://orcid.org/0000-0002-4976-3557)
- Joëlle E. van Aalst
Institutions
- University Medical Center Groningen (NL)
- University of Groningen (NL)
- The Netherlands Cancer Institute (NL)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.phro.2025.100771
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00