  Deformable registration has been one of the pillars of biomedical image
computing. Conventional approaches refer to the definition of a similarity
criterion that, once endowed with a deformation model and a smoothness
constraint, determines the optimal transformation to align two given images.
The definition of this metric function is among the most critical aspects of
the registration process. We argue that incorporating semantic information (in
the form of anatomical segmentation maps) into the registration process will
further improve the accuracy of the results. In this paper, we propose a novel
weakly supervised approach to learn domain specific aggregations of
conventional metrics using anatomical segmentations. This combination is
learned using latent structured support vector machines (LSSVM). The learned
matching criterion is integrated within a metric free optimization framework
based on graphical models, resulting in a multi-metric algorithm endowed with a
spatially varying similarity metric function conditioned on the anatomical
structures. We provide extensive evaluation on three different datasets of CT
and MRI images, showing that learned multi-metric registration outperforms
single-metric approaches based on conventional similarity measures.
