Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis. The unsupervised deep learning registration method can quickly achieve high registration accuracy without labels. However, these methods generally suffer from uncorrelated features, poor ability to register large deformations and details, and unnatural deformation fields. To address the issues above, we propose an unsupervised multi-scale correlation iterative registration network (SearchMorph). In the proposed network, we introduce a correlation layer to strengthen the relevance between features and construct a correlation pyramid to provide multi-scale relevance information for the network. We also design a deformation field iterator, which improves the ability of the model to register details and large deformations through the search module and GRU while ensuring that the deformation field is realistic. We use single-temporal brain MR images and multi-temporal echocardiographic sequences to evaluate the model's ability to register large deformations and details. The experimental results demonstrate that the method in this paper achieves the highest registration accuracy and the lowest folding point ratio using a short elapsed time to state-of-the-art.

翻译：在医学图像分析中,未经监督的深层学习登记方法可以迅速实现高注册准确度,然而,这些方法一般都具有不相干的特点,登记大变形和细节的能力差,以及异常变形领域。为了解决上述问题,我们提议建立一个未经监督的多尺度相关迭代登记网络(SearchMorph)。在拟议的网络中,我们引入一个相关层,以加强各特征之间的关联性,并构建一个相关金字塔,为网络提供多尺度相关信息。我们还设计了一个变形场试剂,通过搜索模块和GRU提高模型登记细节和大变形的能力,同时确保变形领域现实。我们使用单时脑MM图像和多时热心序列来评价模型登记大变形和细节的能力。实验结果显示,本文中的方法实现了最高注册准确性和最低折叠点比率,使用短时间到状态。