Kidney　cancer　is　aggressive　cancer　that　accounts　for　a　large　proportion　of　adult　malignancies.　Computed　tomography　(CT)　imaging　is　an　effective　tool　for　kidney　cancer　diagnosis.　Automatic　and　accurate　kidney　and　kidney　tumor　segmentation　in　CT　scans　is　crucial　for　treatment　and　surgery　planning.　However,　kidney　tumors　and　cysts　have　various　morphologies,　with　blurred　edges　and　unpredictable　positions.　Therefore,　precise　segmentation　of　tumors　and　cysts　faces　a　huge　challenge.　Consider　these　difficulties,　we　propose　a　cascaded　deep　neural　network,　which　first　accurately　locate　the　kidney　area　through　2D　U-Net,　and　then　segment　kidneys,　kidney　tumors,　renal　cysts　through　Multi-decoding　Segmentation　Network　(MDS-Net)　from　the　ROI　of　the　kidney.　We　evaluated　our　method　on　the　2021　Kidney　and　Kidney　Tumor　Segmentation　Challenge　(KiTS21)　dataset.　The　method　achieved　Dice　score,　Surface　Dice　and　Tumor　Dice　of　69.4%,　56.9%　and　51.9%　respectively,　in　the　test　cases.　The　model　of　cascade　network　proposed　in　this　paper　has　a　promising　application　prospect　in　kidney　cancer　diagnosis.　©　2022,　Springer　Nature　Switzerland　AG.