Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.