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Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12263)

Abstract

Deformable image registration between Computed Tomography (CT) images and Magnetic Resonance (MR) imaging is essential for many image-guided therapies. In this paper, we propose a novel translation-based unsupervised deformable image registration method. Distinct from other translation-based methods that attempt to convert the multimodal problem (e.g., CT-to-MR) into a unimodal problem (e.g., MR-to-MR) via image-to-image translation, our method leverages the deformation fields estimated from both: (i) the translated MR image and (ii) the original CT image in a dual-stream fashion, and automatically learns how to fuse them to achieve better registration performance. The multimodal registration network can be effectively trained by computationally efficient similarity metrics without any ground-truth deformation. Our method has been evaluated on two clinical datasets and demonstrates promising results compared to state-of-the-art traditional and learning-based methods.

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Acknowledgement

This project was supported by the National Institutes of Health (Grant No. R01EB025964, R01DK119269, and P41EB015898) and the Overseas Cooperation Research Fund of Tsinghua Shenzhen International Graduate School (Grant No. HW2018008).

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Correspondence to Jayender Jagadeesan .

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Xu, Z. et al. (2020). Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_22

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  • DOI: https://doi.org/10.1007/978-3-030-59716-0_22

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