Patient-specific prostate segmentation in kilovoltage images for radiation therapy intrafraction monitoring via deep learning
Markerless segmentation via deep learning may enable real-time image guidance on conventional cancer therapy systems without requiring implanted markers or additional hardware, thereby expanding access to real-time adaptive radiation therapy.
Authors
- Paul Keall (ORCID: https://orcid.org/0000-0003-4803-6507)
- Adam Mylonas (ORCID: https://orcid.org/0000-0001-6941-383X)
- Mark Gardner (ORCID: https://orcid.org/0000-0003-4340-2547)
- Jeremy Booth (ORCID: https://orcid.org/0000-0003-4004-1109)
- Ryan Brown (ORCID: https://orcid.org/0000-0002-7650-9685)
- Zeyao Li (ORCID: https://orcid.org/0000-0002-8408-6280)
- Doan Trang Nguyen (ORCID: https://orcid.org/0000-0003-1581-0359)
- Thomas Eade (ORCID: https://orcid.org/0000-0003-4324-1886)
- Andrew Kneebone (ORCID: https://orcid.org/0000-0002-7629-504X)
- Marco Mueller (ORCID: https://orcid.org/0000-0002-3205-6979)
Institutions
- The University of Sydney (AU)
- Royal North Shore Hospital (AU)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-06-03
- DOI
- https://doi.org/10.1038/s43856-025-00935-2
- Citations
- 4
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 7.53
Funders
- Australian Government
- Cancer Australia
- Cancer Institute NSW
- National Health and Medical Research Council