BackgroundDeep　learning　based　on　segmentation　models　have　been　gradually　applied　in　biomedical　images　and　achieved　state-of-the-art　performance　for　3D　biomedical　segmentation.　However,　most　of　existing　biomedical　segmentation　researches　take　account　of　the　application　cases　with　adapting　a　single　type　of　medical　images　from　the　corresponding　examining　method.　Considering　of　practical　clinic　application　of　the　radiology　examination　for　diseases,　the　multiple　image　examination　methods　are　normally　required　for　final　diagnosis　especially　in　some　severe　diseases　like　cancers.　Therefore,　by　considering　the　cases　of　employing　multi-modal　images　and　exploring　the　effective　multi-modality　fusion　based　on　deep　networks,　we　do　the　research　to　make　full　use　of　complementary　information　of　multi-modal　images　referring　to　the　clinic　experiences　of　radiologists　in　image　analysis.MethodsReferring　to　the　human　radiologist　diagnosis　experience,　we　discuss　and　propose　a　new　self-attention　aware　mechanism　to　improve　the　segmentation　performance　by　paying　the　different　attention　on　different　modal　images　and　different　symptoms.　Firstly,　we　propose　a　multi-path　encoder　and　decoder　deep　network　for　3D　biomedical　segmentation.　Secondly,　to　leverage　the　complementary　information　among　different　modalities,　we　introduce　a　structure　of　attention　mechanism　called　the　Multi-Modality　Self-Attention　Aware　(MMSA)　convolution.　Multi-modal　images　we　used　in　the　paper　are　different　modalities　of　MR　scanning　images,　which　are　input　into　the　network　separately.　Then　self-attention　weight　fusion　of　multi-modal　features　is　performed　with　our　proposed　MMSA,　which　can　adaptively　adjust　the　fusion　weights　according　to　the　learned　contribution　degree　of　different　modalities　and　different　features　revealing　the　different　symptoms　from　the　labeled　data.ResultsExperiments　have　been　done　on　the　public　competition　dataset　BRATS-2015.　The　results　show　that　our　proposed　method　achieves　dice　scores　of　0.8726,　0.6563,　0.8313　for　the　whole　tumor,　the　tumor　core　and　the　enhancing　tumor　core,　respectively.　Comparing　with　the　U-Net　with　SE　block,　the　scores　are　increased　by　0.0212,0.031,0.0304.ConclusionsWe　present　a　multi-modality　self-attention　aware　convolution,　which　have　better　segmentation　results　based　on　the　adaptive　weighting　fusion　mechanism　with　exploiting　the　multiple　medical　image　modalities.　Experimental　results　demonstrate　the　effectiveness　of　our　method　and　prominent　application　in　the　multi-modality　fusion　based　medical　image　analysis.