Estimating Model and Data Dependant Uncertainty for Synthetic-CTs obtained using Generative Adversarial Networks

The feasibility of using a neural network model to place uncertainty estimates on synthetic-CTs created with a generative adversarial network was investigated. Dropout-based variational inference was employed to account for the uncertainty on the trained model. The standard GAN loss function was also combined with an additional log-likelihood term, designed such that the network learns which regions of input data lead to highly variable output. On a dataset of n=105 brain patients, our results demonstrate that the predicted uncertainty can be interpreted as an upper bound on the true error with a confidence of approximately 95%.