At　present,　gastric　cancer　patients　account　for　a　large　proportion　of　all　tumor　patients.　Gastric　tumor　image　segmentation　can　provide　a　reliable　additional　basis　for　the　clinical　analysis　and　diagnosis　of　gastric　cancer.　However,　the　existing　gastric　cancer　image　datasets　have　disadvantages　such　as　small　data　sizes　and　difficulty　in　labeling.　Moreover,　most　existing　CNN-based　methods　are　unable　to　generate　satisfactory　segmentation　masks　without　accurate　labels,　which　are　due　to　the　limited　context　information　and　insufficient　discriminative　feature　maps　obtained　after　the　consecutive　pooling　and　convolution　operations.　This　paper　presents　a　gastric　cancer　lesion　dataset　for　gastric　tumor　image　segmentation　research.　A　multiscale　boundary　neural　network　(MBNet)　is　proposed　to　automatically　segment　the　real　tumor　area　in　gastric　cancer　images.　MBNet　adopts　encoder-decoder　architecture.　In　each　stage　of　the　encoder,　a　boundary　extraction　refinement　module　is　proposed　for　obtaining　multi　granular　edge　information　and　refinement　firstly.　Then,　we　build　a　selective　fusion　module　to　selectively　fuse　features　from　the　different　stages.　By　cascading　the　two　modules,　the　richer　context　and　fine-grained　features　of　each　stage　are　encoded.　Finally,　the　astrous　spatial　pyramid　pooling　is　improved　to　obtain　the　remote　dependency　relationship　of　the　overall　context　and　the　fine　spatial　structure　information.　The　experimental　results　show　that　the　accuracy　of　the　model　reaches　92.3%,　the　similarity　coefficient　(DICE)　reaches　86.9%,　and　the　performance　of　the　proposed　method　on　the　CVC-ClinicDB　and　Kvasir-SEG　datasets　also　outperforms　existing　approaches.