SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration
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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.
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