Identification　and　analysis　of　leukocytes　(white　blood　cells,　WBC)　in　blood　smear　images　play　a　vital　role　in　the　diagnosis　of　many　diseases,　including　infections,　leukemia,　and　acquired　immune　deficiency　syndrome　(AIDS).　However,　it　remains　difficult　to　accurately　segment　and　identify　leukocytes　under　variable　imaging　conditions,　such　as　variable　light　conditions　and　staining　degrees,　the　presence　of　dyeing　impurities,　and　large　variations　in　cell　appearances,　e.g.,　size,　color,　and　shape　of　cells.　In　this　paper,　we　propose　an　end-to-end　leukocyte　segmentation　algorithm　that　uses　pixel-level　prior　information　for　supervised　training　of　a　deep　convolutional　neural　network.　Specifically,　a　context-aware　feature　encoder　is　first　introduced　to　extract　multi-scale　leukocyte　features.　Then,　a　feature　refinement　module　based　on　the　residual　network　is　designed　to　extract　more　discriminative　features.　Finally,　a　finer　segmentation　mask　of　leukocytes　is　reconstructed　by　a　feature　decoded　based　on　the　feature　maps.　Quantitative　and　qualitative　comparisons　of　real-world　datasets　show　that　the　proposed　method　achieves　state-of-the-art　leukocyte　segmentation　performance　in　terms　of　both　accuracy　and　robustness.　©　2019,　Springer　Nature　Switzerland　AG.