Traditional　deep　learning　based　medical　image　segmentation　methods　require　a　large　number　of　labeled　datasets,　while　medical　images　lack　datasets　due　to　various　reasons　such　as　patient　privacy,　and　even　more　so　lack　labeled　datasets.　Therefore,　this　paper　proposes　a　medical　image　segmentation　method　based　on　cycle　consistency　data　augmentation.　First,　train　space　transformation　registration　network　and　appearance　transformation　registration　network　based　on　cycle　consistency　respectively.　Then,　use　the　trained　space　transformation　registration　network　and　appearance　transformation　registration　network　to　perform　data　enhancement　on　the　original　dataset.　Finally,　the　augmented　data　is　used　to　train　segmentation　network　to　achieve　medical　image　segmentation.　Experiments　on　public　datasets　show　that　this　method　has　better　segmentation　effect　and　better　image　registration　effect.　©　2023　SPIE.