Diabetic　retinopathy　(DR)　is　a　common　diabetic　complication　that　can　lead　to　blindness　in　severe　cases.　Ultra-wide　(swept　source)　optical　coherence　tomography　angiography(UW-OCTA)　imaging　can　help　ophthalmologists　in　the　diagnosis　of　DR.　Automatic　and　accurate　segmentation　of　the　lesion　area　is　essential　in　the　diagnosis　of　DR.　However,　there　still　remain　several　challenges　for　accurately　segmenting　lesion　areas　from　UW-OCTA:　the　various　lesion　locations,　diverse　morphology　and　blurred　contrast.　To　solve　these　problems,　in　this　paper,　we　propose　a　novel　framework　to　segment　neovascularization(NV),　nonperfusion　areas(NA)　and　intraretinal　microvascular　abnormalities(IMA),　which　consists　of　two　parts:　1)　We　respectively　input　the　images　of　three　lesions　into　three　different　channels　to　achieve　three　different　lesions　segmentation　at　the　same　time;　2)　We　improve　the　traditional　2D　U-Net　by　adding　the　residual　module　and　dilated　convolution.　We　evaluate　the　proposed　method　on　the　Diabetic　Retinopathy　Analysis　Challenge　(DRAC)　in　MICCAI2022.　The　mean　Dice　and　mean　IoU　obtained　by　the　method　in　the　test　cases　are　0.4757　and　0.3538,　respectively.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.