Myocardial　infarction　(MI)　is　a　leading　cause　of　death　and　disability,　underscoring　the　critical　importance　of　accurate　and　effective　assessment　of　myocardial　viability.　However,　existing　CNNs　are　insufficient　for　extracting　long-range　contexts　information,　while　the　computational　cost　of　Transformer　architectures　is　prohibitively　high,　making　segmentation　of　scars　and　edema　in　multi-center　cardiac　MRI　(CMR)　sequences　particularly　challenging.　To　address　this　issue,　we　propose　Mamaba　Enhanced　UNet　(ME-UNet),　a　deep　learning　architecture　that　integrates　the　advantages　of　the　U-Net　model　with　residual　mechanisms　and　state　space　models　(SSMs)　for　the　segmentation　of　myocardial　scars　and　edema.　The　advantage　of　ME-UNet　lies　in　its　Vision　Mamba　block,　which　significantly　enhances　the　model’s　ability　to　extract　global　information.　Additionally,　Enhanced　3D　residual　block　(E3DR)　further　enhances　and　consolidates　the　extraction　of　local　and　spatial　information.　Experimental　results　demonstrate　that　ME-UNet　exhibits　exceptional　performance　on　the　MyoPS　challenge　dataset,　effectively　segmenting　myocardial　scars　and　edema,　thereby　validating　the　efficacy　of　our　framework.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.