Abstract
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.
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The source code is public available in https://github.com/easycui/nuclei_segmentation and licensed under MIT license https://github.com/easycui/nuclei_segmentation/blob/master/LICENSE.
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We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
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Yuxin Cui and Guiying Zhang are equally contributed.
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Cui, Y., Zhang, G., Liu, Z. et al. A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Med Biol Eng Comput 57, 2027–2043 (2019). https://doi.org/10.1007/s11517-019-02008-8
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DOI: https://doi.org/10.1007/s11517-019-02008-8