Automatic　liver　segmentation　is　of　great　significance　for　computer-aided　treatment　and　surgery　of　liver　diseases.　However,　respiratory　motion　often　affects　the　liver,　leading　to　image　artifacts　in　liver　magnetic　resonance　imaging　(MRI)　and　increasing　segmentation　difficulty.　To　overcome　this　issue,　we　propose　a　global　spatial　structure-aware　attention　model　(GSA-Net),　a　robust　segmentation　network　developed　to　overcome　the　difficulties　caused　by　respiratory　motion.　The　GSA-Net　is　an　encoder-decoder　architecture,　which　extracts　spatial　structure　information　from　images　and　identifies　different　objects　using　the　minimum　spanning　tree　algorithm.　The　network＇s　encoder　extracts　multi-scale　image　features　with　the　help　of　an　effective　and　lightweight　channel　attention　module.　The　decoder　then　transforms　these　features　bottom-up　using　tree　filter　modules.　Combined　with　the　boundary　detection　module,　the　segmentation　performance　can　be　further　improved.　We　evaluate　the　effectiveness　of　our　method　on　two　liver　MRI　benchmarks:　one　with　respiratory　artifacts　and　the　other　without.　Numerical　evaluations　on　different　benchmarks　demonstrate　that　GSA-Net　consistently　outperforms　previous　state-of-the-art　models　in　terms　of　segmentation　precision　on　our　respiratory　artifact　dataset,　and　also　achieves　notable　results　on　high-quality　datasets.