Automatic　segmentation　of　kidney　tumors　and　lesions　in　medical　images　is　an　essential　measure　for　clinical　treatment　and　diagnosis.　In　this　work,　we　proposed　a　two-stage　cascade　network　to　segment　three　hierarchical　regions:　kidney,　kidney　tumor　and　cyst　from　CT　scans.　The　cascade　is　designed　to　decompose　the　four-class　segmentation　problem　into　two　segmentation　subtasks.　The　kidney　is　obtained　in　the　first　stage　using　a　modified　3D　U-Net　called　Kidney-Net.　In　the　second　stage,　we　designed　a　fine　segmentation　model,　which　named　Masses-Net　to　segment　kidney　tumor　and　cyst　based　on　the　kidney　which　obtained　in　the　first　stage.　A　multi-dimension　feature　(MDF)　module　is　utilized　to　learn　more　spatial　and　contextual　information.　The　convolutional　block　attention　module　(CBAM)　also　introduced　to　focus　on　the　important　feature.　Moreover,　we　adopted　a　deep　supervision　mechanism　for　regularizing　segmentation　accuracy　and　feature　learning　in　the　decoding　part.　Experiments　with　KiTS2021　testset　show　that　our　proposed　method　achieve　Dice,　Surface　Dice　and　Tumor　Dice　of　0.650,　0.518　and　0.478,　respectively.　©　2022,　Springer　Nature　Switzerland　AG.