Estimating uncertainty in radiation oncology dose prediction with dropout and bootstrap in U-Net models

Abstract

Deep learning models, such as U-Net, can be used to efficiently predict the optimal dose distribution in radiotherapy treatment planning. In this work, we want to supplement the prediction model with a measurement of its uncertainty at each voxel. For this purpose, a full Bayesian approach would, however, be too costly. Instead, we compare, based on their correlation with the actual error, three simpler methods, namely, the dropout, the bootstrap and a modification of the U-Net. These methods can be easily adapted to other architectures. 200 patients with head and neck cancer were used in this work.