Robustness and reproducibility of an artificial intelligence-assisted online segmentation and adaptive planning process for online adaptive radiation therapy

Authors

John W Chapman, Washington University School of Medicine in St. Louis
Dao Lam, Washington University School of Medicine in St. Louis
Bin Cai, University of Texas Southwestern Medical Center at Dallas
Geoffrey D Hugo, Washington University School of Medicine in St. Louis

Journal

Journal of applied clinical medical physics

Publication Date

8-1-2022

Volume

23

Issue

8

First Page

e13702

Document Type

Open Access Publication

DOI

10.1002/acm2.13702

Rights and Permissions

Chapman, JW, Lam, D, Cai, B, Hugo, GD. Robustness and reproducibility of an artificial intelligence-assisted online segmentation and adaptive planning process for online adaptive radiation therapy. J Appl Clin Med Phys. 2022; 23:e13702. https://doi.org/10.1002/acm2.13702 © 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Recommended Citation

Chapman, John W; Lam, Dao; Cai, Bin; and Hugo, Geoffrey D, "Robustness and reproducibility of an artificial intelligence-assisted online segmentation and adaptive planning process for online adaptive radiation therapy." Journal of applied clinical medical physics. 23, 8. e13702 (2022).
https://digitalcommons.wustl.edu/oa_4/43