Generalized Probabilistic U-Net for medical image segementation
This work addresses uncertainty estimation in medical image segmentation for healthcare applications, representing an incremental improvement over existing methods.
The paper tackles the problem of capturing uncertainty in medical image segmentation by extending the Probabilistic U-Net with more general Gaussian distributions, showing that a mixture of Gaussians leads to a statistically significant improvement in the generalized energy distance metric on the LIDC-IDRI dataset.
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