Rapid　and　accurate　identification　of　cancerous　areas　during　surgery　is　crucial　for　guiding　surgical　procedures　and　reducing　postoperative　recurrence　rates.　Dynamic　Cell　Imaging　(DCI)　has　emerged　as　a　promising　alternative　to　traditional　frozen　section　pathology,　offering　high-resolution　displays　of　tissue　structures　and　cellular　characteristics.　However,　challenges　persist　in　segmenting　DCI　images　using　deep　learning　methods,　such　as　color　variation　and　artifacts　between　patches　in　whole　slide　DCI　images,　and　the　difficulty　in　obtaining　precise　annotated　data.　In　this　paper,　we　introduce　a　novel　two-stage　framework　for　DCI　image　generation　and　segmentation.　Initially,　the　Dual　Semantic　Diffusion　Model　(DSDM)　is　specifically　designed　to　generate　high-quality　and　semantically　relevant　DCI　images.　These　images　not　only　serve　as　an　effective　means　of　data　augmentation　to　assist　downstream　segmentation　tasks　but　also　help　in　reducing　the　reliance　on　expensive　and　hard-to-obtain　large　annotated　medical　image　datasets.　Furthermore,　we　reuse　the　pretrained　DSDM　to　extract　diffusion　features,　which　are　then　infused　into　the　segmentation　network　via　a　cross-attention　alignment　module.　This　approach　enables　our　network　to　capture　and　utilize　the　characteristics　of　DCI　images　more　effectively,　thereby　significantly　enhancing　segmentation　results.　Our　method　was　validated　on　the　DCI　dataset　and　compared　with　other　methods　for　image　generation　and　segmentation.　Experimental　results　demonstrate　that　our　method　achieves　superior　performance　in　both　tasks,　proving　the　effectiveness　of　the　proposed　model.