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

By Yuxin Cui, Guiying Zhang, Zhonghao Liu, Zheng Xiong, Jianjun Hu. 2018

125 citations recorded by the source corpus.

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 The neural network for nuclei segmentation

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Epidemiology, Medical Imaging

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Citations in the corpus

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images (2018, background): “[26] proposed a network to predict the inner nuclear instance, the nuclear contour and the background.”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Some authors have tried to solve the problem of overlapping nuclei by designing CNNs that predict both objects and their contours (i.e. three-class output: inside, contours, background) [180,190].”