Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients
nnU-Net was able to automatically segment individual lymph nodes on MRI. The detection rate of lymph nodes using nnU-Net was higher than manual segmentations. Re-training nnU-Net was required to successfully transfer the network to the other medical center.
Authors
- Harini Veeraraghavan
- Matteo Maspero (ORCID: https://orcid.org/0000-0003-0347-3375)
- Kaveh Zakeri (ORCID: https://orcid.org/0000-0003-3921-480X)
- Floris C.J. Reinders (ORCID: https://orcid.org/0000-0002-1153-3625)
- M.E.P. Philippens (ORCID: https://orcid.org/0000-0003-0205-3459)
- M.H.F. Savenije (ORCID: https://orcid.org/0000-0001-8170-2546)
- Nancy Y. Lee (ORCID: https://orcid.org/0000-0003-3044-9522)
- Eric Aliotta (ORCID: https://orcid.org/0000-0002-7666-0254)
- Patricia Doornaert (ORCID: https://orcid.org/0000-0002-9475-246X)
- Cornelis P.J. Raaijmakers (ORCID: https://orcid.org/0000-0002-1242-2118)
- Aneesh Rangnekar (ORCID: https://orcid.org/0000-0002-0079-9495)
- Mischa de Ridder (ORCID: https://orcid.org/0000-0002-2530-3038)
- Chris H.J. Terhaard
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Utrecht University (NL)
- University Medical Center Utrecht (NL)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2024-09-28
- DOI
- https://doi.org/10.1016/j.phro.2024.100655
- Citations
- 4
- Primary Topic
- Head and Neck Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.32
Funders
- Memorial Sloan-Kettering Cancer Center
- Stichting de Drie Lichten