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The Conditional Random Field as a Recurrent Neural Network layer is arecently proposed algorithm meant to be placed on top of an existingFully-Convolutional Neural Network to improve the quality of semanticsegmentation. In this paper, we test whether this algorithm, which was shown toimprove semantic segmentation for 2D RGB images, is able to improvesegmentation quality for 3D multi-modal medical images. We developed animplementation of the algorithm which works for any number of spatialdimensions, input/output image channels, and reference image channels. As faras we know this is the first publicly available implementation of this sort. Wetested the algorithm with two distinct 3D medical imaging datasets, weconcluded that the performance differences observed were not statisticallysignificant. Finally, in the discussion section of the paper, we go into thereasons as to why this technique transfers poorly from natural images tomedical images.

Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation