We introduce a strategy for learning image registration without acquired
imaging data, producing powerful networks agnostic to contrast introduced by
magnetic resonance imaging (MRI). While classical registration methods
accurately estimate the spatial correspondence between images, they solve an
optimization problem for every new image pair. Learning-based techniques are
fast at test time but limited to registering images with contrasts and
geometric content similar to those seen during training. We propose to remove
this dependency on training data by leveraging a generative strategy for
diverse synthetic label maps and images that exposes networks to a wide range
of variability, forcing them to learn more invariant features. This approach
results in powerful networks that accurately generalize to a broad array of MRI
contrasts. We present extensive experiments with a focus on 3D neuroimaging,
showing that this strategy enables robust and accurate registration of
arbitrary MRI contrasts even if the target contrast is not seen by the networks
during training. We demonstrate registration accuracy surpassing the state of
the art both within and across contrasts, using a single model. Critically,
training on arbitrary shapes synthesized from noise distributions results in
competitive performance, removing the dependency on acquired data of any kind.
Additionally, since anatomical label maps are often available for the anatomy
of interest, we show that synthesizing images from these dramatically boosts
performance, while still avoiding the need for real intensity images. Our code
is available at this https URL

通过引入一个生成策略来培训图像配准的网络，从而能使网络在不同的 MRI 对比度下精确地适配，在 3D 神经影像方面表现出色，并且不需要依赖于真实的图像数据和解剖标签，其代码可从网址获取。