| Image registration has great application value in the field of computer and medicine.However,the traditional registration algorithm has slow iteration speed,poor universality and low registration accuracy,which is difficult to meet the requirements of real-time registration in clinical field.In recent years,with the rapid development of deep learning,convolutional neural networks have been widely used in the field of image registration.Image registration based on deep learning is mainly divided into two methods: similarity measurement based on image pairs and direct use of deep regression network to predict deformation field.The former method is easily affected by outliers in data sets,and the processing timeliness is poor when iterative calculation of registration parameter estimation or nonlinear registration is carried out.The latter method uses deep neural regression network to directly predict the conversion parameters.The registration speed is fast and the accuracy is high.This paper follows the registration mode of directly predicting the deformation field,and studies the single-mode MR brain image registration technology based on deep learning.The main contents are as follows:1)The data set used in the experiment is preprocessed.In order to remove different tissue information and interference information that are not of practical significance to the registration work of this paper,such as skull and neck,firstly,the brain extraction and resampling of MR brain map are carried out,leaving only the region of interest in image registration.Secondly,the image data of different sizes and uneven gray distribution range are cropped and normalized,and the affine alignment is carried out after scaling the voxel.This paper also adopts the method of deformation to realize data enhancement and improve the robustness and generalization ability of the model.2)An unsupervised deformable image registration algorithm based on distributed feedback network is proposed.In this paper,the framework of the registration network is improved,and the matching U-Net feedback medium network is built.The distributed feedback registration network FIR-Net is obtained by iteratively cascading the U-Net network generation and feedback.By increasing the number of cascades and iterations of feedback blocks in FIR-Net,the high-order deformation information is used to correct the low-level representation,so that the high-level and low-level information of MR feature maps can be fully integrated.Finally,a powerful high-level representation is extracted for obtaining the spatial vector of the deformation field,and the deformation information of higher-level registration image pairs is obtained to improve the registration accuracy.3)An unsupervised deformable registration algorithm combining feedback and attention mechanism is proposed.In order to improve the instability of the loss function combined with U-Net and feedback mechanism during network training,the feedback module is integrated with the channel and spatial attention mechanism,so that the network can learn the remote dependence and improve the stability of gradient descent in longdistance output.In addition,in order to alleviate the difficulty of high similarity of image anatomical structure to fine registration,the spatial feedback registration network SF-Net is obtained by cascading the polarization self-attention mechanism within the feedback network,which reduces the semantic barrier between feature maps in high and low order information fusion,and promotes the ability of network learning to obtain key deformation information,and further improves the registration accuracy.In this paper,sufficient experiments are carried out on three brain MR image datasets and quantitative and visual analysis are carried out to prove the feasibility of the proposed algorithm in medical image registration tasks. |
