Delineating　the　brain　tumor　from　magnetic　resonance　(MR)　images　is　critical　for　the　treatment　of　gliomas.　However,　automatic　delineation　is　challenging　due　to　the　complex　appearance　and　ambiguous　outlines　of　tumors.　Considering　that　multi-modal　MR　images　can　reflect　different　tumor　biological　properties,　we　develop　a　novel　multi-modal　tumor　segmentation　network　(MMTSN)　to　robustly　segment　brain　tumors　based　on　multi-modal　MR　images.　The　MMTSN　is　composed　of　three　sub-branches　and　a　main　branch.　Specifically,　the　sub-branches　are　used　to　capture　different　tumor　features　from　multi-modal　images,　while　in　the　main　branch,　we　design　a　spatial-channel　fusion　block　(SCFB)　to　effectively　aggregate　multi-modal　features.　Additionally,　inspired　by　the　fact　that　the　spatial　relationship　between　sub-regions　of　the　tumor　is　relatively　fixed,　e.g.,　the　enhancing　tumor　is　always　in　the　tumor　core,　we　propose　a　spatial　loss　to　constrain　the　relationship　between　different　sub-regions　of　tumor.　We　evaluate　our　method　on　the　test　set　of　multi-modal　brain　tumor　segmentation　challenge　2020　(BraTs2020).　The　method　achieves　0.8764,　0.8243　and　0.773　Dice　score　for　the　whole　tumor,　tumor　core　and　enhancing　tumor,　respectively.　©　2021,　Springer　Nature　Switzerland　AG.