Impact of contrast-enhanced agent on segmentation using a deep learning-based software “Ai-Seg” for head and neck cancer
We developed a segmentation model for HN OARs dedicated to CE CT using Ai-Seg and evaluated its usability on nCE CT.
Authors
- Shoki Inui (ORCID: https://orcid.org/0000-0002-5691-8457)
- Takashi Akagi (ORCID: https://orcid.org/0000-0002-5157-6099)
- Toshiki Ikawa (ORCID: https://orcid.org/0000-0001-9097-0227)
- Sayaka Kihara (ORCID: https://orcid.org/0000-0002-6319-7707)
- Shuichi Harada
- Naoyuki Kanayama (ORCID: https://orcid.org/0000-0003-0016-2213)
- Yoshihiro Ueda (ORCID: https://orcid.org/0000-0001-7151-9094)
- Teiji Nishio (ORCID: https://orcid.org/0009-0006-0538-0235)
- Akira Masaoka
- Koji Konishi
Institutions
- Osaka International Cancer Institute (JP)
- Osaka Health Science University (JP)
- Hyogo Ion Beam Medical Center (JP)
- Osaka International Cancer Institute (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1093/bjr/tqaf108
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00