  Image segmentation relies heavily on neural networks which are known to be
overconfident, especially when making predictions on out-of-distribution (OOD)
images. This is a common scenario in the medical domain due to variations in
equipment, acquisition sites, or image corruptions. This work addresses the
challenge of OOD detection by proposing Laplacian Segmentation Networks (LSN):
methods which jointly model epistemic (model) and aleatoric (data) uncertainty
for OOD detection. In doing so, we propose the first Laplace approximation of
the weight posterior that scales to large neural networks with skip connections
that have high-dimensional outputs. We demonstrate on three datasets that the
LSN-modeled parameter distributions, in combination with suitable uncertainty
measures, gives superior OOD detection.
