人工智能
分割
卷积神经网络
计算机科学
边界(拓扑)
图像分割
任务(项目管理)
图像(数学)
模式识别(心理学)
计算机视觉
人工神经网络
深度学习
算法
数学
数学分析
经济
管理
作者
Yuxin Cui,Guiying Zhang,Zhonghao Liu,Zheng Xiong,Jianjun Hu
标识
DOI:10.1007/s11517-019-02008-8
摘要
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
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