The reference in the caption of Figure 6 was erroneously cited as Maas et al. (104). It should be Ronneberger et al. (59).
The caption of Figure 6 has been updated to “Architecture of the U-Net. Reproduced with permission from Ronneberger et al. (59).”
The reference in the caption of Figure 9 was erroneously cited as Alexander et al. (227).
It should be Wang et al. (224).
The sentence “Reprinted from Alexander et al. (227).” has been corrected to “Reproduced with permission from Wang et al. (224).” in the caption of Figure 9.
Reference 224 was erroneously written as “Wang S, Chen Y, Jarvis LA, Tang Y, Gladstone DJ, Samkoe KS, et al. Robust Real-time segmentation of bio-morphological features in Human Cherenkov imaging during radiotherapy via deep learning. arXiv [Preprint]. arXiv:2409.05666v1 (2014). doi: 10.1002/mp.18002”. It should be “Wang S, Chen Y, Jarvis LA, Tang Y, Gladstone DJ, Samkoe KS, et al. Robust real-time segmentation of bio-morphological features in human Cherenkov imaging during radiotherapy via deep learning. Med Phys. (2025) 52:e18002. doi: 10.1002/mp.18002.”
Reference 1 was incorrectly cited in section 6 Image synthesis in RT, 6.1 Deep learning networks in medical images, Paragraph 2. Reference 59 should have been cited.
The sentence previously read: “One of the most well-known CNN models is the U-shaped net (U-Net) proposed by Ronneberger et al. (1) (Figure 6).”
The sentence now reads: “One of the most well-known CNN models is the U-shaped net (U-Net) proposed by Ronneberger et al. (59) (Figure 6).”
The original version of this article has been updated.
Statements
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
Wang S Chen Y Jarvis LA Tang Y Gladstone DJ Samkoe KS et al . Robust real-time segmentation of bio-morphological features in human Cherenkov imaging during radiotherapy via deep learning. Med Phys. (2025) 52:e18002. doi: 10.1002/mp.18002
Summary
Keywords
magnetic resonance imaging guided radiotherapy, positron emission tomography, stereoscopic imaging and surface guidance, cone beam computed tomography, generative image synthesis, Cherenkov radiation imaging, imaging innovations in proton therapy, advanced quantitative imaging
Citation
Yan Y, Alexander DA, Bednarz BP, Bronk LF, Chen H, Gladstone DJ, Han B, Iannuzzi CM, Li Y, Nguyen N, Mulenga N, Viscariello NN, Wang Y, Weygand J, Zlateva Y and Guan F (2025) Correction: Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy. Front. Med. 12:1738811. doi: 10.3389/fmed.2025.1738811
Received
03 November 2025
Accepted
11 November 2025
Published
25 November 2025
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
12 - 2025
Updates
Copyright
© 2025 Yan, Alexander, Bednarz, Bronk, Chen, Gladstone, Han, Iannuzzi, Li, Nguyen, Mulenga, Viscariello, Wang, Weygand, Zlateva and Guan.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Fada Guan, fguan@mdanderson.org; Yue Yan, yue.yan@dartmouth.edu
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.