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Cell image segmentation is an important topic since it benefits for medical research and diagnosis. It is more challenging than other segmentation problems because cells have similar colors and their boundaries are not always obvious. In recent years, deep learning based methods, including the V-net, play an important role in image segmentation. In this paper, we proposed several techniques to further improve the performance of the V-net for cell segmentation. First, in addition to (i) cells and (ii) background, we add two labels: (iii) the edge between cells and background and (iv) the edges among cells. Since the properties of cell edges are quite different from those of cell bodies and background, adding these labels are helpful for improving the performance. Moreover, several morphology-based post-processing algorithms are applied for boundary refinement. With these techniques, the accuracies of cell segmentation can be much improved and the cells with poor contrast can still be well segmented.