Significant breakthroughs in medical image registration have been achieved using deep neural networks(DNNs).However,DNN-based end-to-end registration methods often require large quantities of data or adequate annotations for training.To leverage the intensity information of abundant unlabeled images,unsupervised registration methods commonly employ intensity-based similarity measures to optimize the network parameters.However,finding a sufficiently robust measure can be challenging for specific registration applications.Weakly supervised registration methods use anatomical labels to estimate the deformation between images.High-level structural information in label images is more reliable and practical for estimating the voxel correspondence of anatomic regions of interest between images,whereas label images are extremely difficult to collect.In this paper,we propose a two-stage semi-supervised learning framework for medical image registration,which consists of unsupervised and weakly supervised registration networks.The proposed semi-supervised learning framework is trained with intensity information from available images,label information from a relatively small number of labeled images and pseudo-label information from unlabeled images.Experimental results on two datasets(cardiac and abdominal images)demonstrate the efficacy and efficiency of this method in intra-and inter-modality medical image registrations,as well as its superior performance when a vast amount of unlabeled data and a small set of annotations are available.Our code is publicly available at .

JIA Dengqiang;LUO Xinzhe;DING Wangbin;HUANG Liqin;ZHUANG Xiahai

School of Naval Architecture,Ocean and Civil Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Data Science,Fudan University,Shanghai 200433,China;College of Physics and Information Engineering,Fuzhou University,Fuzhou 350117,China

上海交通大学学报（英文版）

[1]JIA Dengqiang;LUO Xinzhe;DING Wangbin;HUANG Liqin;ZHUANG Xiahai-.SeRN:A Two-Stage Framework of Registration for Semi-Supervised Learning for Medical Images)[J].上海交通大学学报（英文版）,2022(02):176-189

SeRN,registrations

Two,Stage,Framework,Registration,Semi,Supervised,Learning,Medical,Images,Significant,breakthroughs,medical,have,been,achieved,using,deep,neural,networks,DNNs,However,end,methods,often,require,large,quantities,adequate,annotations,training,To,leverage,intensity,information,abundant,unlabeled,images,unsupervised,commonly,employ,similarity,measures,optimize,parameters,finding,sufficiently,robust,challenging,specific,applications,Weakly,use,anatomical,labels,estimate,deformation,between,High,level,structural,more,reliable,practical,estimating,voxel,correspondence,regions,interest,whereas,are,extremely,difficult,collect,In,this,paper,stage,semi,learning,framework,which,consists,weakly,proposed,trained,from,available,relatively,small,number,pseudo,Experimental,results,datasets,cardiac,abdominal,demonstrate,efficacy,efficiency,intra,modality,well,its,superior,performance,when,vast,amount,Our,code,publicly

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