Kidney　cancer　is　one　of　the　most　common　malignancies　worldwide.　Early　diagnosis　is　an　effective　way　to　reduce　the　mortality　and　automated　segmentation　of　kidney　tumor　in　computed　tomography　scans　is　an　important　way　to　assisted　kidney　cancer　diagnosis.　In　this　paper,　we　propose　a　convolution-and-transformer　network　(COTRNet)　for　end　to　end　kidney,　kidney　tumor,　and　kidney　cyst　segmentation.　COTRNet　is　an　encoder-decoder　architecture　where　the　encoder　and　the　decoder　are　connected　by　skip　connections.　The　encoder　consists　of　four　convolution-transformer　layers　to　learn　multi-scale　features　which　have　local　and　global　receptive　fields　crucial　for　accurate　segmentation.　In　addition,　we　leverage　pretrained　weights　and　deep　supervision　to　further　improve　segmentation　performance.　Experimental　results　on　the　2021　kidney　and　kidney　tumor　segmentation　(kits21)　challenge　demonstrated　that　our　method　achieved　average　dice　of　61.6%,　surface　dice　of　49.1%,　and　tumor　dice　of　50.52%,　respectively,　which　ranked　the　22(th)　place　on　the　kits21　challenge.