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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 9; pp. 2027 - 2044 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138201475&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138201475 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2019 vid: 57 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138201475 138201475 NLM31346949 10.1007/s11517-019-02008-8 NLM31346949 138201475 ppf: 2027 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. aug: au: Cui, Yuxin Zhang, Guiying Liu, Zhonghao Xiong, Zheng Hu, Jianjun affil: Department of Computer Science and Technology, University of South Carolina, 29208, Columbia, SC, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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