  This paper addresses the task of nuclei segmentation in high-resolution
histopathological images. We propose an auto- matic end-to-end deep neural
network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary
model is introduced to predict nuclei and their boundaries simultaneously using
a fully convolutional neural network. Given a color normalized image, the model
directly outputs an estimated nuclei map and a boundary map. A simple, fast and
parameter-free post-processing procedure is performed on the estimated nuclei
map to produce the final segmented nuclei. An overlapped patch extraction and
assembling method is also designed for seamless prediction of nuclei in large
whole-slide images. We also show the effectiveness of data augmentation methods
for nuclei segmentation task. Our experiments showed our method outperforms
prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000
image can be segmented in less than 5 seconds. This makes it possible to
precisely segment the whole-slide image in acceptable time
