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　https://github.com/jdq818/SeRN.　©　2021,　Shanghai　Jiao　Tong　University　and　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.