Gliomas　are　the　most　common　and　aggressive　malignant　primary　brain　tumors.　Automatic　brain　tumor　segmentation　from　multi-modality　magnetic　resonance　images　using　deep　learning　methods　is　critical　for　gliomas　diagnosis.　Deep　learning　segmentation　architectures,　especially　based　on　fully　convolutional　neural　network,　have　proved　great　performance　on　medical　image　segmentation.　However,　these　approaches　cannot　explicitly　model　global　information　and　overlook　the　topology　structure　of　lesion　regions,　which　leaves　room　for　improvement.　In　this　paper,　we　propose　a　convolution-and-transformer　network　(COTRNet)　to　explicitly　capture　global　information　and　a　topology　aware　loss　to　constrain　the　network　to　learn　topological　information.　Moreover,　we　exploit　transfer　learning　by　using　pretrained　parameters　on　ImageNet　and　deep　supervision　by　adding　multi-level　predictions　to　further　improve　the　segmentation　performance.　COTRNet　achieved　dice　scores　of　78.08%,　76.18%,　and　83.92%　in　the　enhancing　tumor,　the　tumor　core,　and　the　whole　tumor　segmentation　on　brain　tumor　segmentation　challenge　2021.　Experimental　results　demonstrated　effectiveness　of　the　proposed　method.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.