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Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images. Since images with different modalities exhibit different features, it remains a challenge to find their exact correspondence. Most of the current methods based on image-to-image translation cannot fully leverage the available information, which will affect the subsequent registration performance. To solve the problem, we develop an unsupervised multimodal image registration method named DTR-GAN. Firstly, we design a multimodal registration framework via a bidirectional translation network to transform the multimodal image registration into a unimodal registration, which can effectively use the complementary information of different modalities. Then, to enhance the quality of the transformed images in the translation network, we design a multiscale encoder–decoder network that effectively captures both local and global features in images. Finally, we propose a mixed similarity loss to encourage the warped image to be closer to the target image in deep features. We extensively evaluate methods for MRI-CT image registration tasks of the abdominal cavity with advanced unsupervised multimodal image registration approaches. The results indicate that DTR-GAN obtains a competitive performance compared to other methods in MRI-CT registration. Compared with DFR, DTR-GAN has not only obtained performance improvements of 2.35% and 2.08% in the dice similarity coefficient (DSC) of MRI-CT registration and CT-MRI registration on the Learn2Reg dataset but has also decreased the average symmetric surface distance (ASD) by 0.33 mm and 0.12 mm on the Learn2Reg dataset.

Multimodal medical image registration has an important role in monitoring tumor growth, radiotherapy, and disease diagnosis. Deep-learning-based methods have made great progress in the past few years. However, its success depends on large training datasets, and the performance of the model decreases due to overfitting and poor generalization when only limited data are available. In this paper, a multimodal medical image registration framework based on few-shot learning is proposed, named reverse-net, which can improve the accuracy and generalization ability of the network by using a few segmentation labels. Firstly, we used the border enhancement network to enhance the ROI (region of interest) boundaries of T1 images to provide high-quality data for the subsequent pixel alignment stage. Secondly, through a coarse registration network, the T1 image and T2 image were roughly aligned. Then, the pixel alignment network generated more smooth deformation fields. Finally, the reverse teaching network used the warped T1 segmentation labels and warped images generated by the deformation field to teach the border enhancement network more structural knowledge. The performance and generalizability of our model have been evaluated on publicly available brain datasets including the MRBrainS13DataNii-Pro, SRI24, CIT168, and OASIS datasets. Compared with VoxelMorph, the reverse-net obtained performance improvements of 4.36% in DSC on the publicly available MRBrainS13DataNii-Pro dataset. On the unseen dataset OASIS, the reverse-net obtained performance improvements of 4.2% in DSC compared with VoxelMorph, which shows that the model can obtain better generalizability. The promising performance on dataset CIT168 indicates that the model is practicable.