Deformable image registration provides dynamic information about the image and is essential in medical image analysis. However, due to the different characteristics of single-temporal brain MR images and multi-temporal echocardiograms, it is difficult to accurately register them using the same algorithm or model. We propose an unsupervised multi-scale correlation iterative registration network (SearchMorph), and the model has three highlights. (1)We introduced cost volumes to strengthen feature correlations and constructed correlation pyramids to complement multi-scale correlation information. (2) We designed the search module to search for the registration of features in multi-scale pyramids. (3) We use the GRU module for iterative refinement of the deformation field. The proposed network in this paper shows leadership in common single-temporal registration tasks and solves multi-temporal motion estimation tasks. The experimental results show that our proposed method achieves higher registration accuracy and a lower folding point ratio than the state-of-the-art methods.

翻译：变形图像登记提供关于图像的动态信息,对医学图像分析至关重要,但是,由于单时脑MR图像和多时回声心电图的不同特点,很难用相同的算法或模型准确登记这些图像。我们提议建立一个无人监督的多尺度相关迭代登记网络(SearchMorph),模型有三个亮点。 (1) 我们引入了成本量,以加强特征相关性和构建相关金字塔,以补充多尺度相关信息。 (2) 我们设计了搜索模块,以搜索多尺度金字塔中特征的登记。 (3) 我们使用GRU模块对变形场进行迭接性改进。本文中拟议的网络显示在共同的单时登记任务中的领先地位,并解决多时运动估计任务。实验结果表明,我们拟议的方法的登记准确性更高,折合点比率低于最新方法。