Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks

• URL: http://arxiv.org/abs/2011.06214v1
• Date: Thu, 12 Nov 2020 05:37:30 GMT
• Title: Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks
• Authors: Zhe Xu, Jiangpeng Yan, Jie Luo, William Wells, Xiu Li, Jayender Jagadeesan
• Abstract summary: We propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods.
• Score: 22.94932232413841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration, to constrain the deformation field of multimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods, especially for severely deformed local regions.

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