Myocardial　pathology　segmentation　(MyoPS)　is　essential　for　the　diagnosis,　treatment,　and　prognosis　of　myocardial　infarction　(MI).　Recent　deep　learning　MyoPS　models　have　shown　promising　performance　on　independent　identically　distributed　data.　However,　due　to　the　domain　shift　caused　by　multi-center　data,　many　methods　often　fail　to　generalize　to　unseen　data.　In　this　work,　we　propose　a　novel　domain　generalization　framework　(SMCANet)　for　MyoPS,　which　leverages　data　statistics　(mean　and　standard　deviation)　modeling　(DSM)　and　feature　covariance　alignment　(FCA)　during　training　to　learn　domain-invariant　information.　Specifically,　DSM　first　models　the　statistical　information　of　the　data　images　and　then　performs　perturbation-based　random　sampling　to　enhance　the　diversity　of　data　representations.　FCA　reduces　feature　discrepancies　across　different　distributions　while　preserving　domain　content　information　through　the　use　of　covariance　matching　loss　(CML)　and　cross-covariance　loss　(CCL).　Experiments　on　cross-domain　MyoPS　datasets　from　CARE2024　MyoPS++　challenge　demonstrate　that　our　framework　can　achieve　promising　generalization　performance　without　altering　the　network　architecture　or　employing　complex　data　augmentation　networks.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.