  CT and MRI are two of the most informative modalities in spinal diagnostics
and treatment planning. CT is useful when analysing bony structures, while MRI
gives information about the soft tissue. Thus, fusing the information of both
modalities can be very beneficial. Registration is the first step for this
fusion. While the soft tissues around the vertebra are deformable, each
vertebral body is constrained to move rigidly. We propose a weakly-supervised
deep learning framework that preserves the rigidity and the volume of each
vertebra while maximizing the accuracy of the registration. To achieve this
goal, we introduce anatomy-aware losses for training the network. We
specifically design these losses to depend only on the CT label maps since
automatic vertebra segmentation in CT gives more accurate results contrary to
MRI. We evaluate our method on an in-house dataset of 167 patients. Our results
show that adding the anatomy-aware losses increases the plausibility of the
inferred transformation while keeping the accuracy untouched.
