Abdominal　computed　tomography　(CT)　is　a　common　modality　to　detect　liver　lesions.　Liver　segmentation　in　CT　scan　is　important　for　diagnosis　and　analysis　of　liver　lesions.　However,　the　accuracy　of　existing　liver　segmentation　methods　is　slightly　insufficient.　In　this　paper,　we　propose　a　liver　segmentation　architecture　named　CR-U-Net,　which　is　composed　of　cascade　U-Net　combined　with　residual　mapping.　We　make　use　of　the　MDice　loss　function　for　training　in　CR-U-Net,　and　the　second-level　of　cascade　network　is　deeper　than　the　first-level　to　extract　more　detailed　image　features.　Morphological　algorithms　are　utilized　as　an　intermediate-processing　step　to　improve　the　segmentation　accuracy.　In　addition,　we　evaluate　our　proposed　CR-U-Net　on　liver　segmentation　task　under　the　dataset　provided　by　the　2017　ISBI　LiTS　Challenge.　The　experimental　result　demonstrates　that　our　proposed　CR-U-Net　can　outperform　the　state-of-The-Art　methods　in　term　of　the　performance　measures,　such　as　Dice　score,　VOE,　and　so　on.　©　2019　IEEE.