Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL
The PHL-IDOL framework applied to the auto-segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient-specific prior information in a task central to online ART workflows.
Authors
- Janet M. Denbeigh (ORCID: https://orcid.org/0000-0002-7640-2618)
- Chris Beltran (ORCID: https://orcid.org/0000-0001-7688-0815)
- Bo Lü (ORCID: https://orcid.org/0000-0002-3807-7869)
- Xiaoying Liang (ORCID: https://orcid.org/0000-0002-6688-1045)
- Alessio Parisi (ORCID: https://orcid.org/0000-0002-9323-8012)
- Sridhar Yaddanapudi (ORCID: https://orcid.org/0000-0001-9796-8576)
- Justin C. Park
- Keith M. Furutani (ORCID: https://orcid.org/0000-0002-6794-2540)
- Sven Olberg
- Jin Sung Kim (ORCID: https://orcid.org/0000-0003-1415-6471)
- Jun Tan (ORCID: https://orcid.org/0000-0002-2281-5599)
- Bongyong Song (ORCID: https://orcid.org/0000-0001-9364-7628)
- Byong Su Choi
Institutions
- Harvard University (US)
- Yonsei University (KR)
- University of California San Diego (US)
- Massachusetts General Hospital (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-08-21
- DOI
- https://doi.org/10.1002/mp.17361
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.22
Funders
- National Research Foundation
- National Research Foundation of Korea