  A novel non-rigid image registration algorithm is built upon fully
convolutional networks (FCNs) to optimize and learn spatial transformations
between pairs of images to be registered in a self-supervised learning
framework. 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. The image registration is implemented in a
multi-resolution image registration framework to jointly optimize and learn
spatial transformations and FCNs at different spatial resolutions with deep
self-supervision through typical feedforward and backpropagation computation.
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.
