  Nuclear segmentation is important and frequently demanded for pathology image
analysis, yet is also challenging due to nuclear crowdedness and possible
occlusion. In this paper, we present a novel bottom-up method for nuclear
segmentation. The concepts of Center Mask and Center Vector are introduced to
better depict the relationship between pixels and nuclear instances. The
instance differentiation process are thus largely simplified and easier to
understand. Experiments demonstrate the effectiveness of Center Vector
Encoding, where our method outperforms state-of-the-arts by a clear margin.
