An Unsupervised Learning Model for Fast Deformable Medical Image Registration

We present an efficient learning-based algorithm for deformable,
pairwise 3D medical image registration. Current registration methods
optimize an energy function independently for each pair of images,
which can be time-consuming for large data. We define registration as
a parametric function, and optimize its parameters given a set of
images from a collection of interest. Given a new pair of scans, we
can quickly compute a registration field by directly evaluating the
function using the learned parameters. We model this function using a
CNN, and use a spatial transform layer to reconstruct one image from
another while imposing smoothness constraints on the registration
field. The proposed method does not require supervised information
such as ground truth registration fields or anatomical landmarks. We
demonstrate registration accuracy comparable to state-of-the-art 3D
image registration, while operating orders of magnitude faster in
practice. Our method promises to significantly speed up medical image
analysis and processing pipelines, while facilitating novel directions
in learning-based registration and its applications.

The pre-print is available at https://arxiv.org/abs/1802.02604