  Deformable image registration is a fundamental task in medical image
analysis, aiming to establish a dense and non-linear correspondence between a
pair of images. Previous deep-learning studies usually employ supervised neural
networks to directly learn the spatial transformation from one image to
another, requiring task-specific ground-truth registration for model training.
Due to the difficulty in collecting precise ground-truth registration,
implementation of these supervised methods is practically challenging. Although
several unsupervised networks have been recently developed, these methods
usually ignore the inherent inverse-consistent property (essential for
diffeomorphic mapping) of transformations between a pair of images. Also,
existing approaches usually encourage the to-be-estimated transformation to be
locally smooth via a smoothness constraint only, which could not completely
avoid folding in the resulting transformation. To this end, we propose an
Inverse-Consistent deep Network (ICNet) for unsupervised deformable image
registration. Specifically, we develop an inverse-consistent constraint to
encourage that a pair of images are symmetrically deformed toward one another,
until both warped images are matched. Besides using the conventional smoothness
constraint, we also propose an anti-folding constraint to further avoid folding
in the transformation. The proposed method does not require any supervision
information, while encouraging the diffeomoprhic property of the transformation
via the proposed inverse-consistent and anti-folding constraints. We evaluate
our method on T1-weighted brain magnetic resonance imaging (MRI) scans for
tissue segmentation and anatomical landmark detection, with results
demonstrating the superior performance of our ICNet over several
state-of-the-art approaches for deformable image registration. Our code will be
made publicly available.
