Fast deformable image registration uncertainty estimation for contour propagation in daily adaptive proton therapy
Andreas Smolders, Florian Amstutz, Ye Zhang, Damien Charles Weber, Tony Lomax, Francesca Albertini
In daily adaptive proton therapy, deformable image registration (DIR) can be used to propagate manually delineated contours from a reference CT to the daily CT for plan reoptimization. However, the ill-posedness of DIR implies uncertainty on the DIR hyperparameters, which results in uncertainty in the displacement field. In this work, a fast deep learning method is developed to predict the uncertainty associated with a DIR result without the need for Monte-Carlo (MC) sampling. It is shown that this results in a significant time reduction compared to MC whilst leading to similar probabilistic contours.
Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)