# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 1 Introduction
In the field of multimodal registration, many methods are based on similarity metric training. [PERSON] et al. [11] proposed an unsupervised dual-channel registration network to resolve the deformation between preoperative MR and intraoperative CT. The model achieved excellent results in MR-CT registration. Since the above registration methods ignore the inverse consistency of the transform between a pair of images and the deformation field is only required to be locally smooth, the overlap of the deformation field cannot be completely avoided. Therefore, [PERSON] et al. [12] proposed to avoid folding in the transform by combining the traditional smoothness constraint with the anti-folding constraint. [PERSON] et al. [13] used GAN networks for multimodal retinal and cardiac image registration; the model outperformed the reverse deep learning-based B spline method by combining conditional and cyclic constraints [14]. For multimodal registration, it is difficult to correlate two modal images because the intensity of each modal image is different. [PERSON] et al. [15] used image intensities and gradients to align each image pair for real-time registration. The above unsupervised registration methods do not require ground-truth and have attracted a lot of attention from researchers in recent years. However, unsupervised registration is easily misaligned on blurry structures and warped on task-independent backgrounds. Therefore, it is crucial to develop few-shot learning. Few-shot learning trains a model from limited labeled data and reduces the need for data [16]. In medical image analysis, few-shot learning is urgently needed due to the lack of medical image data, and it has been successful in many scenarios. For example, [PERSON] et al. [17] proposed few-shot learning deformable medical image registration framework. It achieved satisfactory results on three datasets using only five segmented labeled data. In addition, few-shot learning has been successful in medical image segmentation [18], object detection [19], and histopathology image classification [20]. Inspired by the above methods, we propose a framework for deformable image registration based on few-shot learning. By redesigning the network structure, our model achieves satisfactory registration performance. The main work is as follows:  
1. We present a multimodal few-shot learning framework for deformable medical image registration. The border enhancement network is used to enhance the ROI boundaries of T1 images and provide quality data for subsequent pixel alignment network. The coarse registration network roughly aligns the T1 image with the T2 image, which can reduce image discrepancies. The pixel alignment network generates more smooth deformation field. The reverse teaching network teaches border enhancement network rich structural knowledge. By combining the four networks, the model can achieve better registration performance by using few segmentation labels. 2. We introduce reverse teaching which can improve the performance of the border enhancement network with only few segmentation labels. It utilizes the deformation field generated by the pixel alignment network to generate additional training data (warped images and warped segmentation label pairs), thus it reversely teaches the border enhancement network more structural knowledge. Therefore, the border enhancement network with better performance will provide clearer image of the ROI boundary for the pixel alignment network, finally improving the registration performance. 3. The model is trained on the IXI dataset and evaluated on the MRBrainSI3 DataNiNiPro), SRI24, CIT168, and OASIS datasets. The experimental results show that the model has better performance and generalization ability.