Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

• URL: http://arxiv.org/abs/2011.06216v1
• Date: Thu, 12 Nov 2020 05:47:20 GMT
• Title: Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance
• Authors: Zhe Xu, Jiangpeng Yan, Jie Luo, Xiu Li, Jayender Jagadeesan
• Abstract summary: Unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. The estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. We propose a novel multimodal registration framework, which leverages the deformation fields estimated from both.
• Score: 23.461130560414805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.

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