DISA: DIfferentiable Similarity Approximation for Universal Multimodal
  Registration
Matteo Ronchetti Wolfgang Wein Nassir Navab Oliver Zettinig Raphael Prevost
Multimodal image registration is a challenging but essential step for
numerous image-guided procedures. Most registration algorithms rely on the
computation of complex, frequently non-differentiable similarity metrics to
deal with the appearance discrepancy of anatomical structures between imaging
modalities. Recent Machine Learning based approaches are limited to specific
anatomy-modality combinations and do not generalize to new settings. We propose
a generic framework for creating expressive cross-modal descriptors that enable
fast deformable global registration. We achieve this by approximating existing
metrics with a dot-product in the feature space of a small convolutional neural
network (CNN) which is inherently differentiable can be trained without
registered data. Our method is several orders of magnitude faster than local
patch-based metrics and can be directly applied in clinical settings by
replacing the similarity measure with the proposed one. Experiments on three
different datasets demonstrate that our approach generalizes well beyond the
training data, yielding a broad capture range even on unseen anatomies and
modality pairs, without the need for specialized retraining. We make our
training code and data publicly available.