Accurate　liver　segmentation　is　essential　for　the　diagnosis　and　treatment　planning　of　liver　diseases.　Recently,　deep-learning-based　segmentation　methods　have　achieved　state-of-the-art　performance　in　the　medical　image　domain.　However,　due　to　low　soft　tissue　contrast　between　the　liver　and　its　surrounding　organs,　most　existing　segmentation　methods　are　difficult　to　capture　the　boundary　information　of　the　liver　well.　Considering　boundary　information　is　necessary　for　liver　edge　localization　in　liver　segmentation,　we　propose　a　novel　deep-learning-based　segmentation　network,　called　Boundary　Preserving　Dual　Attention　network　(BPDA).　In　the　proposed　BPDA,　the　boundary　is　being　concerned　with　the　dual　attention　module　which　consists　of　the　spatial　attention　module　and　the　semantic　attention　module　at　each　layer　of　U-Net　with　the　feature　maps　of　current　and　its　neighboring　layer　as　inputs.　The　spatial　attention　module　enhances　the　perception　of　target　boundaries　by　focusing　on　salient　intensity　changes.　Meanwhile,　the　semantic　attention　module　highlights　the　contribution　of　different　filters　in　semantic　feature　extraction　by　weighting　important　channels.　Moreover,　to　overcome　the　phenomena　that　the　segmentation　of　left　lobe　performance　bad　especially　in　new　datasets　from　different　sources　in　practical　use,　we　analyze　the　attribution　of　the　liver,　we　devise　a　data　augmentation　strategy　to　expand　the　data　of　the　left　lobe　of　the　liver　under　the　guidance　of　professional　radiologists.　The　comparative　experiments　have　done　on　the　self-build　clinic　liver　dataset,　which　includes　156　clinic　case　images　with　Iterative　Decomposition　of　Water　and　Fat　with　Echo　Asymmetry　and　Least-squares　Estimation　Magnetic　Resonance　Imaging(IDEAL-IQ　MRI).　Experiments　show　that　our　proposed　method　outperforms　other　state-of-the-art　segmentation　methods.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.