Recently,　CT　perfusion　(CTP)　has　been　used　to　triage　ischemic　stroke　patients　in　the　early　stage,　because　of　its　speed,　availability,　and　lack　of　contraindications.　But　CTP　data　alone,　even　with　the　generated　perfusion　maps　is　not　enough　to　describe　the　precise　location　of　infarct　core　or　penumbra.　Considering　the　good　performance　demonstrated　on　Diffusion　Weighted　Imaging　(DWI),　We　propose　a　CTP　data　analysis　technique　using　Generative　Adversarial　Networks　(GAN)　[2]　to　generate　DWI,　and　segment　the　regions　of　ischemic　stroke　lesion　on　top　of　the　generated　DWI　based　on　convolutional　neutral　network　(CNN)　and　a　novel　loss　function.　Specifically,　our　CNN　structure　consists　of　a　generator,　a　discriminator　and　a　segmentator.　The　generator　synthesizes　DWI　from　CT,　which　generates　a　high　quality　representation　for　subsequent　segmentator.　Meanwhile　the　discriminator　competes　with　the　generator　to　identify　whether　its　input　DWI　is　real　or　generated.　And　we　propose　a　novel　segmentation　loss　function　that　contains　a　weighted　cross-entropy　loss　and　generalized　dice　loss　[1]　to　balance　the　positive　and　negative　loss　in　the　training　phase.　And　the　weighted　cross　entropy　loss　can　highlight　the　area　of　stroke　lesion　to　enhance　the　structural　contrast.　We　also　introduce　other　techniques　in　our　network,　like　GN　[13],　Maxout　[14]　in　the　proposed　network.　Data　augmentation　is　also　used　in　the　training　phase.　In　our　experiments,　an　average　dice　coefficient　of　60.65%　is　achieved　with　four-fold　cross-validation.　From　the　results　of　our　experiments,　this　novel　network　combined　with　the　proposed　loss　function　achieved　a　better　performance　in　CTP　data　analysis　than　traditional　methods　using　CTP　perfusion　parameters.　©　Springer　Nature　Switzerland　AG　2019.