  Deep learning based methods provide efficient solutions to medical image
registration, including the challenging problem of diffeomorphic image
registration. However, most methods register normal image pairs, facing
difficulty handling those with missing correspondences, e.g., in the presence
of pathology like tumors. We desire an efficient solution to jointly account
for spatial deformations and appearance changes in the pathological regions
where the correspondences are missing, i.e., finding a solution to metamorphic
image registration. Some approaches are proposed to tackle this problem, but
they cannot properly handle large pathological regions and deformations around
pathologies. In this paper, we propose a deep metamorphic image registration
network (MetaRegNet), which adopts time-varying flows to drive spatial
diffeomorphic deformations and generate intensity variations. We evaluate
MetaRegNet on two datasets, i.e., BraTS 2021 with brain tumors and 3D-IRCADb-01
with liver tumors, showing promising results in registering a healthy and tumor
image pair. The source code is available online.
