A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images

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...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2019-09, Vol.57 (9), p.2027-2043
Main Authors: Cui, Yuxin, Zhang, Guiying, Liu, Zhonghao, Xiong, Zheng, Hu, Jianjun
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Boundary maps
Computer Applications
Databases, Factual
Deep Learning
Female
Histocytochemistry - methods
Histopathology
Human Physiology
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image resolution
Image segmentation
Imaging
Male
Neoplasms - pathology
Neural networks
Neural Networks, Computer
Nuclei
Original Article
Post-production processing
Radiology
Source code
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Summary: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 1000×1000 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 . Graphical Abstract The neural network for nuclei segmentation
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-019-02008-8