Diabetic　retinopathy(DR)　is　the　major　cause　of　blindness,　and　the　pathogenesis　is　unknown.　Ultra-wide　optical　coherence　tomography　angiography　imaging　(UW-OCTA)　can　help　ophthalmologists　to　diagnose　DR.　Automatic　and　accurate　segmentation　of　lesions　is　essential　for　the　diagnosis　of　DR,　yet　accurate　identification　and　segmentation　of　lesions　from　UW-OCTA　images　remains　a　challenge.　We　proposed　a　modified　nnUNet　named　nnUNet-CBAM.　Three　networks　were　trained　to　segment　each　lesion　separately.　Our　method　was　evaluated　in　DRAC2022　diabetic　retinopathy　analysis　challenge,　where　segmentation　results　were　tested　on　65　UW-OCTA　images.　These　images　are　standardized　UW-OCTA.　Our　method　achieved　a　mean　dice　similarity　coefficient　(mDSC)　of　0.4963　and　a　mean　intersection　over　union　(mIOU)　of　0.3693.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.