  Image registration and in particular deformable registration methods are
pillars of medical imaging. Inspired by the recent advances in deep learning,
we propose in this paper, a novel convolutional neural network architecture
that couples linear and deformable registration within a unified architecture
endowed with near real-time performance. Our framework is modular with respect
to the global transformation component, as well as with respect to the
similarity function while it guarantees smooth displacement fields. We evaluate
the performance of our network on the challenging problem of MRI lung
registration, and demonstrate superior performance with respect to state of the
art elastic registration methods. The proposed deformation (between inspiration
& expiration) was considered within a clinically relevant task of interstitial
lung disease (ILD) classification and showed promising results.
