Accurate　automatic　segmentation　of　cardiac　MRI　images　can　be　used　for　clinical　parameter　calculation　and　provide　visual　guidance　for　surgery,　which　is　important　for　both　diagnosis　and　treatment　of　cardiac　diseases.　Existing　automatic　segmentation　methods　for　cardiac　MRI　are　based　on　U-shaped　network　structure　introducing　global　pooling,　attention　and　etc.　operations　to　extract　more　effective　features.　These　approaches,　however,　suffer　from　a　mismatch　between　sensing　receptive　field　and　resolution　and　neglect　to　pay　attention　to　object　boundaries.　In　this　paper,　we　propose　a　new　boundary　attentive　multi-scale　network　based　on　U-shaped　network　for　automatic　segmentation　of　cardiac　MRI　images.　Effective　features　are　extracted　based　on　channel　attention　for　shallow　features.　With　the　goal　of　increasing　segmentation　accuracy,　multi-scale　features　are　extracted　using　densely　coupled　multi-scale　dilated　convolutions.　In　order　to　improve　the　ability　to　learn　the　precise　boundary　of　the　objects,　a　gated　boundary-aware　branch　is　introduced　and　utilized　to　concentrate　on　the　object　border　region.　The　effectiveness　and　robustness　of　the　network　are　confirmed　by　evaluating　this　method　on　the　ACDC　cardiac　MRI　dataset　to　produce　segmentation　predictions　for　the　left　ventricle,　right　ventricle,　and　myocardial.　Comparative　studies　demonstrate　that　our　suggested　method　produces　superior　segmentation　outcomes　when　compared　to　other　cardiac　MRI　segmentation　methods.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.