SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration

• URL: http://arxiv.org/abs/2206.13076v1
• Date: Mon, 27 Jun 2022 06:37:02 GMT
• Title: SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration
• Authors: Xiao Fan, Shuxin Zhuang, Zhemin Zhuang, Shunmin Qiu, Alex Noel Joseph Raj and Yibiao Rong
• Abstract summary: We propose an unsupervised multi-scale correlation iterative registration network (SearchMorph) Our proposed method achieves higher registration accuracy and a lower folding point ratio than the state-of-the-art methods.
• Score: 2.417545540754701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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