A Disentangled Representations based Unsupervised Deformable Framework for Cross-modality Image Registration

Cross-modality magnetic resonance image (MRI) registration is a fundamental step in various MRI analysis tasks. However, it remains challenging due to the domain shift between different modalities. In this paper, we proposed a fully unsupervised deformable framework for cross-modality image registration through image disentangling. To be specific, MRIs of both modalities are decomposed into a shared domain-invariant content space and domain-specific style spaces via a multi-modal unsupervised image-to-image translation approach. An unsupervised deformable network is then built based on the assumption that intrinsic information in the content space is preserved among different modalities. In addition, we proposed a novel loss function consists of two metrics, with one defined in the original image space and the other in the content space. Validation experiments were performed on two datasets. Compared to two conventional state-of-the-art cross-modality registration methods, the proposed framework shows a superior registration performance.Clinical relevance—This work can serve as an auxiliary tool for cross-modality registration in clinical practice.