3D Segmentation of Glial Cells Using Fully Convolutional Networks and k-Terminal Cut

Lin Yang, Yizhe Zhang, Ian H. Guldner, Siyuan Zhang, Danny Z. Chen

Abstract: 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 k-terminal 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.