We propose a fully unsupervised multi-modal deformable image registration
method (UMDIR), which does not require any ground truth deformation fields or
any aligned multi-modal image pairs during training. Multi-modal registration
is a key problem in many medical image analysis applications. It is very
challenging due to complicated and unknown relationships between different
modalities. In this paper, we propose an unsupervised learning approach to
reduce the multi-modal registration problem to a mono-modal one through image
disentangling. In particular, we decompose images of both modalities into a
common latent shape space and separate latent appearance spaces via an
unsupervised multi-modal image-to-image translation approach. The proposed
registration approach is then built on the factorized latent shape code, with
the assumption that the intrinsic shape deformation existing in original image
domain is preserved in this latent space. Specifically, two metrics have been
proposed for training the proposed network: a latent similarity metric defined
in the common shape space and a learningbased image similarity metric based on
an adversarial loss. We examined different variations of our proposed approach
and compared them with conventional state-of-the-art multi-modal registration
methods. Results show that our proposed methods achieve competitive performance
against other methods at substantially reduced computation time.

本文提出了一种基于无监督学习的多模态图像配准方法，采用图像分解的方法将多模态图像分解成通用潜在形状空间和独立潜在外观空间，并且通过因式分解潜在形状代码来进行配准，提出了两种评估网络的指标并与传统的多模态注册方法进行了比较，结果表明我们的方法在大大缩短计算时间的情况下取得了与其他方法竞争性的性能。