Automatic　analysis　of　histopathological　whole　slide　images　(WSIs)　is　a　challenging　task.　In　this　paper,　we　designed　two　deep　learning　structures　based　on　a　fully　convolutional　network　(FCN)　and　a　convolutional　neural　network　(CNN),　to　achieve　the　segmentation　of　carcinoma　regions　from　WSIs.　FCN　is　developed　for　segmentation　problems　and　CNN　focuses　on　classification.　We　designed　experiments　to　compare　the　performances　of　the　two　methods.　The　results　demonstrated　that　CNN　performs　as　well　as　FCN　when　applied　to　WSIs　in　high　resolution.　Furthermore,　to　leverage　the　advantages　of　CNN　and　FCN,　we　integrate　the　two　methods　to　obtain　a　complete　framework　for　lung　cancer　segmentation.　The　proposed　methods　were　evaluated　on　the　ACDC-LungHP　dataset.　The　final　dice　coefficient　for　cancerous　region　segmentation　is　0.770.　©　2019,　Springer　Nature　Switzerland　AG.