  Deformable registration consists of finding the best dense correspondence
between two different images. Many algorithms have been published, but the
clinical application was made difficult by the high calculation time needed to
solve the optimisation problem. Deep learning overtook this limitation by
taking advantage of GPU calculation and the learning process. However, many
deep learning methods do not take into account desirable properties respected
by classical algorithms.
  In this paper, we present MICS, a novel deep learning algorithm for medical
imaging registration. As registration is an ill-posed problem, we focused our
algorithm on the respect of different properties: inverse consistency, symmetry
and orientation conservation. We also combined our algorithm with a multi-step
strategy to refine and improve the deformation grid. While many approaches
applied registration to brain MRI, we explored a more challenging body
localisation: abdominal CT. Finally, we evaluated our method on a dataset used
during the Learn2Reg challenge, allowing a fair comparison with published
methods.
