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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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Published in: | Medical & biological engineering & computing 2019-09, Vol.57 (9), p.2027-2043 |
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description | 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 |
doi_str_mv | 10.1007/s11517-019-02008-8 |
format | article |
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https://github.com/easycui/nuclei_segmentation
.
Graphical Abstract
The neural network for nuclei segmentation</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-019-02008-8</identifier><identifier>PMID: 31346949</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>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</subject><ispartof>Medical & biological engineering & computing, 2019-09, Vol.57 (9), p.2027-2043</ispartof><rights>International Federation for Medical and Biological Engineering 2019</rights><rights>Medical & Biological Engineering & Computing is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-8592a24cdb5c24993632ad2f46a7b41a53ac3be9214edbee933f25c86c0b79d83</citedby><cites>FETCH-LOGICAL-c375t-8592a24cdb5c24993632ad2f46a7b41a53ac3be9214edbee933f25c86c0b79d83</cites><orcidid>0000-0002-8725-6660</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2263793229/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2263793229?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11687,27923,27924,36059,36060,44362,74666</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31346949$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cui, Yuxin</creatorcontrib><creatorcontrib>Zhang, Guiying</creatorcontrib><creatorcontrib>Liu, Zhonghao</creatorcontrib><creatorcontrib>Xiong, Zheng</creatorcontrib><creatorcontrib>Hu, Jianjun</creatorcontrib><title>A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>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</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Boundary maps</subject><subject>Computer Applications</subject><subject>Databases, Factual</subject><subject>Deep Learning</subject><subject>Female</subject><subject>Histocytochemistry - methods</subject><subject>Histopathology</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Imaging</subject><subject>Male</subject><subject>Neoplasms - pathology</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Nuclei</subject><subject>Original Article</subject><subject>Post-production 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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</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31346949</pmid><doi>10.1007/s11517-019-02008-8</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-8725-6660</orcidid></addata></record> |
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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 |
title | A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images |
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