CoCycleReg: Collaborative cycle-consistency method for multi-modal medical image registration

Multi-modal image registration is an essential step for many medical image analysis applications. Recent advances in multi-modal image registration rely on image-to-image translation to achieve good performance. However, the performance is still limited owing to the poor use of complementary regularization between image registration and translation, which is able to simultaneously enhance both parts’ accuracy. To this end, we propose CoCycleReg, a novel method that formulates image registration and translation in a Collaborative Cycle-consistency manner. Instead of dividing into two discrete stages, we unify the image registration and translation via cycle-consistency in an end-to-end training process, such that each part can benefit from the other one. To ensure the deformation fields’ reversibility in the cycle, we extensively introduce a novel dual-head registration network, consisting of one single backbone to extract the features and two heads to respectively predict the deformation fields. The experiments on T1-T2(MRI) and CT-MRI datasets validate that the proposed CoCycleReg surpasses the other state-of-the-art conventional and deep learning approaches comprehensively considering the speed, accuracy, and regularity of deformation fields. In the ablation analysis, a method that sets the cycle-consistency Corresponding authors at: Department of Computer Science at School of Informatics, Xiamen University, Xiamen 361005, Chinaconstraints of registration and image-to-image translation separately is compared, and the results demonstrate the effectiveness of collaborative cycle-consistency. In addition, the improvement of image-to-image translation is also verified in further analysis. The code is publicly available at https://github.com/DopamineLcy/cocycle-reg/.