Automated confidence estimation in deep learning auto‐segmentation for brain organs at risk on MRI for radiotherapy
ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.
Authors
- Bashar Al‐Qaisieh (ORCID: https://orcid.org/0000-0003-0745-784X)
- Michael Nix (ORCID: https://orcid.org/0000-0001-7228-7344)
- Ann Henry (ORCID: https://orcid.org/0000-0002-5379-6618)
- Louise Murray (ORCID: https://orcid.org/0000-0003-0658-6455)
- Nouf M. Alzahrani
Institutions
- University of Leeds (GB)
- King Abdulaziz University (SA)
- St James's University Hospital (GB)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1002/acm2.14513
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.22
Funders
- Yorkshire Cancer Research
- Cancer Research UK