Deep　learning　(DL)-based　models　have　demonstrated　good　performance　in　medical　image　segmentation.　However,　the　models　trained　on　a　known　dataset　often　fail　when　performed　on　an　unseen　dataset　collected　from　different　centers,　vendors　and　disease　populations.　In　this　work,　we　present　a　random　style　transfer　network　to　tackle　the　domain　generalization　problem　for　multi-vendor　and　center　cardiac　image　segmentation.　Style　transfer　is　used　to　generate　training　data　with　a　wider　distribution/heterogeneity,　namely　domain　augmentation.　As　the　target　domain　could　be　unknown,　we　randomly　generate　a　modality　vector　for　the　target　modality　in　the　style　transfer　stage,　to　simulate　the　domain　shift　for　unknown　domains.　The　model　can　be　trained　in　a　semi-supervised　manner　by　simultaneously　optimizing　a　supervised　segmentation　and　a　unsupervised　style　translation　objective.　Besides,　the　framework　incorporates　the　spatial　information　and　shape　prior　of　the　target　by　introducing　two　regularization　terms.　We　evaluated　the　proposed　framework　on　40　subjects　from　the　M&Ms　challenge2020,　and　obtained　promising　performance　in　the　segmentation　for　data　from　unknown　vendors　and　centers.　©　2021,　Springer　Nature　Switzerland　AG.