Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation

• URL: http://arxiv.org/abs/2204.13656v1
• Date: Thu, 28 Apr 2022 17:18:21 GMT
• Title: Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation
• Authors: Zekang Chen, Jia Wei and Rui Li
• Abstract summary: We propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes.
• Score: 4.43142018105102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets \cite{hering2021learn2reg}. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.

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