  We propose a semantic similarity metric for image registration. Existing
metrics like Euclidean Distance or Normalized Cross-Correlation focus on
aligning intensity values, giving difficulties with low intensity contrast or
noise. Our approach learns dataset-specific features that drive the
optimization of a learning-based registration model. We train both an
unsupervised approach using an auto-encoder, and a semi-supervised approach
using supplemental segmentation data to extract semantic features for image
registration. Comparing to existing methods across multiple image modalities
and applications, we achieve consistently high registration accuracy. A learned
invariance to noise gives smoother transformations on low-quality images.
