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.

本文提出了一种新型的基于卷积神经网络的非刚性图像配准算法，通过优化和学习不同尺度下的空间变换以及卷积神经网络，实现了对 3D 结构脑磁共振图像更好的配准效果。

无需训练数据的基于自监督全卷积网络的非刚性图像配准

Non-Rigid Image Registration Using Self-Supervised Fully Convolutional  Networks without Training Data

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.

本文提出了一种基于全卷积网络优化并学习图像之间空间变换的新型非刚性图像配准算法，实现了多分辨率下空间变换和网络学习的联合优化，并且在注册 3D 结构性脑磁共振（MR）图像方面表现比最先进的图像配准算法更好。