基于无监督学习的医学图像配准算法综述OA

Medical image registration is a fundamental task in the field of medical image analysis and has been widely used in clinical applications such as disease progression tracking,atlas construction,statistical analysis,and surgical navi-gation.However,traditional registration algorithms are limited by their relatively low computational efficiency,making it difficult to meet the diverse application demands nowadays.In recent years,deep learning-based medical image registra-tion methods have gradually become a major research focus in the industry.Furthermore,due to the difficulty in obtaining annotated datasets with realistic deformation fields,how to construct registration models through unsupervised learning has emerged as a prominent research topic.By reviewing relevant literature on unsupervised medical image registration over the past decade,this paper categorizes the mainstream registration frameworks into three types:single-stream,cas-cade,and pyramid architectures.Based on this categorization,it systematically elaborates on the development trajectory,key deep learning technologies,and future directions of unsupervised medical image registration.