A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images
复制标题
DOI：
10.1007/s11517-019-02008-8
复制
发表时间：
2019-09-01
影响因子：
3.2
通讯作者：
Hu, Jianjun
中科院分区：
文献类型：
--
作者：
Cui, Yuxin;Zhang, Guiying;Hu, Jianjun
本文讨论了高分辨率组织病理学图像中细胞核分割的任务。提出了一种自动端到端深度神经网络分割单个核的算法。引入核边界模型，利用全卷积神经网络同时预测核及其边界。给定颜色归一化图像，该模型直接输出估计的核图和边界图。对估计的核图进行简单、快速、无参数的后处理，得到最终的分割核。设计了一种重叠斑块提取和组装方法，实现了大尺寸全片图像核的无缝预测。我们还证明了数据增强方法在核分割任务中的有效性。我们的实验表明，我们的方法优于先前最先进的方法。此外，在不到5秒的时间内，可以高效地分割出一张1000x1000的图像。这使得在可接受的时间内精确分割整个幻灯片图像成为可能。源代码可从https://github.com/easycui/nuclei_segmentation获得。
This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation 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 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation.