Abstract
We propose a novel non-rigid image registration algorithm that is built upon
fully convolutional networks (FCNs) to optimize and learn spatial
transformations between pairs of images to be registered. Different from most
existing deep learning based image registration methods that learn spatial
transformations from training data with known corresponding spatial
transformations, our method directly estimates spatial transformations between
pairs of images by maximizing an image-wise similarity metric between fixed and
deformed moving images, similar to conventional image registration algorithms.
At the same time, our method also learns FCNs for encoding the spatial
transformations at the same spatial resolution of images to be registered,
rather than learning coarse-grained spatial transformation information. The
image registration is implemented in a multi-resolution image registration
framework to jointly optimize and learn spatial transformations and FCNs at
different resolutions with deep self-supervision through typical feedforward
and backpropagation computation. Since our method simultaneously optimizes and
learns spatial transformations for the image registration, our method can be
directly used to register a pair of images, and the registration of a set of
images is also a training procedure for FCNs so that the trained FCNs can be
directly adopted to register new images by feedforward computation of the
learned FCNs without any optimization. The proposed method has been evaluated
for registering 3D structural brain magnetic resonance (MR) images and obtained
better performance than state-of-the-art image registration algorithms.
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