The　counting　and　identification　of　white　blood　cells　(WBCs,　i.e.,　leukocytes)　in　blood　smear　images　play　a　crucial　role　in　the　diagnosis　of　certain　diseases,　including　leukemia,　infections,　and　COVID-19　(corona　virus　disease　2019).　WBC　image　segmentation　lays　a　firm　foundation　for　automatic　WBC　counting　and　identification.　However,　automated　WBC　image　segmentation　is　challenging　due　to　factors　such　as　background　complexity　and　variations　in　appearance　caused　by　histological　staining　conditions.　To　improve　WBC　image　segmentation　accuracy,　we　propose　a　deep　learning　network　called　WBC-Net,　which　is　based　on　UNet++　and　ResNet.　Specifically,　WBC-Net　designs　a　context-aware　feature　encoder　with　residual　blocks　to　extract　multi-scale　features,　and　introduces　mixed　skip　pathways　on　dense　convolutional　blocks　to　obtain　and　fuse　image　features　at　different　scales.　Moreover,　WBC-Net　uses　a　decoder　incorporating　convolution　and　deconvolution　to　refine　the　WBC　segmentation　mask.　Furthermore,　WBC-Net　defines　a　loss　function　based　on　cross-entropy　and　the　Tversky　index　to　train　the　network.　Experiments　on　four　image　datasets　show　that　the　proposed　WBC-Net　achieves　better　WBC　segmentation　performance　than　several　state-of-the-art　methods.　©　2020　Elsevier　B.V.