  Label propagation is a popular technique for anatomical segmentation. In this
work, we propose a novel deep framework for label propagation based on
non-local label fusion. Our framework, named CompareNet, incorporates subnets
for both extracting discriminating features, and learning the similarity
measure, which lead to accurate segmentation. We also introduce the voxel-wise
classification as an unary potential to the label fusion function, for
alleviating the search failure issue of the existing non-local fusion
strategies. Moreover, CompareNet is end-to-end trainable, and all the
parameters are learnt together for the optimal performance. By evaluating
CompareNet on two public datasets IBSRv2 and MICCAI 2012 for brain
segmentation, we show it outperforms state-of-the-art methods in accuracy,
while being robust to pathologies.
