  Registration of images with pathologies is challenging due to tissue
appearance changes and missing correspondences caused by the pathologies.
Moreover, mass effects as observed for brain tumors may displace tissue,
creating larger deformations over time than what is observed in a healthy
brain. Deep learning models have successfully been applied to image
registration to offer dramatic speed up and to use surrogate information (e.g.,
segmentations) during training. However, existing approaches focus on learning
registration models using images from healthy patients. They are therefore not
designed for the registration of images with strong pathologies for example in
the context of brain tumors, and traumatic brain injuries. In this work, we
explore a deep learning approach to register images with brain tumors to an
atlas. Our model learns an appearance mapping from images with tumors to the
atlas, while simultaneously predicting the transformation to atlas space. Using
separate decoders, the network disentangles the tumor mass effect from the
reconstruction of quasi-normal images. Results on both synthetic and real brain
tumor scans show that our approach outperforms cost function masking for
registration to the atlas and that reconstructed quasi-normal images can be
used for better longitudinal registrations.
