  The Conditional Random Field as a Recurrent Neural Network layer is a
recently proposed algorithm meant to be placed on top of an existing
Fully-Convolutional Neural Network to improve the quality of semantic
segmentation. In this paper, we test whether this algorithm, which was shown to
improve semantic segmentation for 2D RGB images, is able to improve
segmentation quality for 3D multi-modal medical images. We developed an
implementation of the algorithm which works for any number of spatial
dimensions, input/output image channels, and reference image channels. As far
as we know this is the first publicly available implementation of this sort. We
tested the algorithm with two distinct 3D medical imaging datasets, we
concluded that the performance differences observed were not statistically
significant. Finally, in the discussion section of the paper, we go into the
reasons as to why this technique transfers poorly from natural images to
medical images.
