  We propose the Generalized Probabilistic U-Net, which extends the
Probabilistic U-Net by allowing more general forms of the Gaussian distribution
as the latent space distribution that can better approximate the uncertainty in
the reference segmentations. We study the effect the choice of latent space
distribution has on capturing the uncertainty in the reference segmentations
using the LIDC-IDRI dataset. We show that the choice of distribution affects
the sample diversity of the predictions and their overlap with respect to the
reference segmentations. For the LIDC-IDRI dataset, we show that using a
mixture of Gaussians results in a statistically significant improvement in the
generalized energy distance (GED) metric with respect to the standard
Probabilistic U-Net. We have made our implementation available at
https://github.com/ishaanb92/GeneralizedProbabilisticUNet
