  Fully-connected Conditional Random Field (CRF) is often used as
post-processing to refine voxel classification results by encouraging spatial
coherence. In this paper, we propose a new end-to-end training method called
Posterior-CRF. In contrast with previous approaches which use the original
image intensity in the CRF, our approach applies 3D, fully connected CRF to the
posterior probabilities from a CNN and optimizes both CNN and CRF together. The
experiments on white matter hyperintensities segmentation demonstrate that our
method outperforms CNN, post-processing CRF and different end-to-end training
CRF approaches.
