  Spatial transformations are enablers in a variety of medical image analysis
applications that entail aligning images to a common coordinate systems.
Population analysis of such transformations is expected to capture the
underlying image and shape variations, and hence these transformations are
required to produce anatomically feasible correspondences. This is usually
enforced through some smoothness-based generic regularization on deformation
field. Alternatively, population-based regularization has been shown to produce
anatomically accurate correspondences in cases where anatomically unaware
(i.e., data independent) fail. Recently, deep networks have been for
unsupervised image registration, these methods are computationally faster and
maintains the accuracy of state of the art methods. However, these networks use
smoothness penalty on deformation fields and ignores population-level
statistics of the transformations. We propose a novel neural network
architecture that simultaneously learns and uses the population-level
statistics of the spatial transformations to regularize the neural networks for
unsupervised image registration. This regularization is in the form of a
bottleneck autoencoder, which encodes the population level information of the
deformation fields in a low-dimensional manifold. The proposed architecture
produces deformation fields that describe the population-level features and
associated correspondences in an anatomically relevant manner and are
statistically compact relative to the state-of-the-art approaches while
maintaining computational efficiency. We demonstrate the efficacy of the
proposed architecture on synthetic data sets, as well as 2D and 3D medical
data.

Computer Vision and Pattern Recognition;Machine Learning