The　segmentation　of　the　lesion　region　in　gastroscopic　images　is　highly　important　for　the　detection　and　treatment　of　early　gastric　cancer.　This　paper　proposes　a　novel　approach　for　gastric　lesion　segmentation　by　using　generative　adversarial　training.　First,　a　segmentation　network　is　designed　to　generate　accurate　segmentation　masks　for　gastric　lesions.　The　proposed　segmentation　network　adds　residual　blocks　to　the　encoding　and　decoding　path　of　U-Net.　The　cascaded　dilated　convolution　is　also　added　at　the　bottleneck　of　U-Net.　The　residual　connection　promotes　information　propagation,　while　dilated　convolution　integrates　multi-scale　context　information.　Meanwhile,　a　discriminator　is　used　to　distinguish　the　generated　and　real　segmentation　masks.　The　proposed　discriminator　is　a　Markov　discriminator　(Patch-GAN),　which　discriminates　each　NxN　matrix　in　the　image.　In　the　process　of　network　training,　the　adversary　training　mechanism　is　used　to　iteratively　optimize　the　generator　and　the　discriminator　until　they　converge　at　the　same　time.　The　experimental　results　show　that　the　dice,　accuracy,　and　recall　are　86.6%,　91.9%,　and　87.3%,　respectively.　These　metrics　are　significantly　better　than　the　existing　models,　which　proves　the　effectiveness　of　this　method　and　can　meet　the　needs　of　clinical　diagnosis　and　treatment.