Quantifying model uncertainty for semantic segmentation of Fluorine-19 MRI using stochastic gradient MCMC

Fluorine-19 ((19)F) MRI is an emerging theranostic tool for studying diseases and treatments simultaneously, particularly in challenging neuroinflammatory conditions. However, the low signal-to-noise ratio (SNR) of (19)F MRI necessitates computational methods to reliably detect (19)F signal regions and segment these from the background. In this study, we demonstrate that Bayesian fully convolutional neural networks provide a means to increase sensitivity in (19)F MRI and simultaneously provide estimates of data uncertainty. While our model effectively denoises the data, uncertain areas remain, particularly in boundary regions of the foreground. The uncertainty estimates are beneficial in preventing overconfident downstream analysis on noisy data and providing crucial information for rectifying prediction errors. Our results demonstrate that our model significantly outperforms other commonly used methods for (19)F MRI signal detection in terms of sensitivity, while also providing valuable uncertainty estimates.