# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 1 Introduction  
Medical image registration technology is the basis for accurate image fusion. Medical imaging techniques can be broadly divided into two categories: structural imaging and functional imaging. Structural imaging generally refers to X-rays, computed tomography (CT), and so on, which have high spatial resolution and can reflect the anatomical structure of tissues, while functional imaging generally refers to positron emission computed tomography (PET), single-photon emission computed tomography (SPECT), and so on, which have poor spatial resolution but can record the information of tissue metabolism and neural activity. In the process of diagnosis, it is often necessary to fuse the medical image information of different modalities to improve the accuracy of clinical diagnosis. However, due to different imaging devices, different acquisition moments, and the different acquisition perspectives of medical images of different modalities, the spatial location of images of the same object in different modalities is shifted. To obtain effective fusioninformation, it is necessary to use image registration techniques to align the images of different modalities. Deformable image registration can be divided into traditional methods and deep learning-based methods. Although conventional methods such as SyN [1], Demons [2], and optical flow [3] methods have achieved satisfactory performance after continuous parameter tuning, the higher time cost is not suitable for clinical treatment. Compared with traditional methods, deep-learning-based methods can provide fast registration [4] and improve registration accuracy through powerful feature extraction and learning capabilities. Deep-learning-based methods can be divided into supervised methods and unsupervised methods. Supervised registration is achieved by minimizing the difference between the generated deformation field and the ground-truth using elaborate ground-truth. [PERSON] et al. [5] used mean square error to train regression neural networks for slice-to-volume registration and volume-to-volume registration scenarios. The model was applied to the registration between the T1 images of an infant brain and the T2 images of a fetus brain. [PERSON] et al. [6] proposed an adversarial image registration model, in which the generator directly estimated the transformation parameter, and the discriminator determined whether the image pair was aligned. By introducing Euclidean distance and adversarial loss to construct the loss function, the model achieved better performance on the MR-TRUS dataset. Recent advances in using organ anatomy labels facilitate medical image registration. [PERSON] et al. [7] proposed a model that utilizes the complementary nature of the joint model of medical image registration and segmentation to bring improvements in the complex scenes and few-shot situation. The diversity of the enhanced data is maintained by embedding a perturbation factor in the alignment to increase the activity of the deformation. The model finally achieved excellent results. [PERSON] et al. [8] provided a high-quality segmentation label for subsequent MR-CBCT multimodal registration using APA2 Seg-Net. [PERSON] et al. [9] proposed to align the surrounding structure by combining the complementary advantages of segmentation labels and local distance metrics. Compared with label-driven deep learning frameworks, its Dice scores were significantly improved. The above models achieve better performance, but the performance of the registration depends on the quality of the labels. Since segmentation labels are labeled by experienced medical experts, it is necessary to develop an unsupervised registration framework that does not require labels. Unsupervised registration is achieved by reducing the difference between fixed and warped images. [PERSON] et al. introduced adaptive convolution and adaptive transformer to enhance the ability of global semantic extraction. Compared with other fusion methods, satisfactory fusion results have been achieved [10].