Accurate　and　reliable　automatic　segmentation　of　lesion　areas　in　gastroscopy　images　can　assist　endoscopists　in　making　diagnoses　and　reduce　the　possibility　of　missed　or　incorrect　diagnoses.　This　paper　presents　a　two-stage　framework　for　segmenting　gastroscopy　images,　which　aims　to　improve　the　accuracy　of　medical　image　segmentation　tasks　using　limited　datasets.　The　proposed　framework　consists　of　two　stages:　the　Image　Enhancement　Stage　and　the　Lesion　Segmentation　Stage.　First,　in　the　Image　Enhancement　Stage,　an　image　enhancement　solution　called　TDC-Enhance　is　proposed　to　enrich　the　original　small-scale　gastroscopy　image　dataset.　This　solution　performs　Texture　Enhancement,　Detail　Enhancement,　and　Color　Enhancement　on　the　original　images.　Then,　in　the　Lesion　Segmentation　Stage,　a　multi-path　automatic　segmentation　network　for　gastroscopy　images,　named　DynaSiam,　is　introduced.　DynaSiam　comprises　a　Dependent　Encoder,　a　Shared　Encoder,　and　a　Fusion　Decoder.　It　learns　feature　information　related　to　the　lesion　region　by　encoding　the　different　enhanced　images　obtained　in　the　Image　Enhancement　Stage　as　inputs　to　the　multi-path　network.　Additionally,　a　Dynamic　Feature　Interaction　(DFI)　block　is　designed　to　capture　and　learn　deeper　image　information,　thereby　improving　the　segmentation　performance　of　the　model.　The　experimental　results　show　that　the　proposed　method　achieves　a　90.80%　mIoU,　92.71%　Dice　coefficient　and　96.31%　Accuracy.　Other　performance　metrics　also　indicate　the　best　performance,　suggesting　that　the　proposed　model　has　significant　potential　for　clinical　analysis　and　diagnosis.　Code　and　implementation　details　can　be　found　on　GitHub:　https://github.com/kyasulee/DynaSiam.