Estimating Uncertainty in Radiation Oncology Dose Prediction with Dropout and Bootstrap in U-Net Models

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.