Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images
In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus...
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Veröffentlicht in: | IEEE transactions on image processing 2018-12, Vol.27 (12), p.5759-5774 |
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description | In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset. |
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Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2018.2857001</identifier><identifier>PMID: 30028701</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Active contours ; Algorithms ; Animals ; Automation ; Boundaries ; cascade sparse regression chain model ; Cell Line ; Cell Nucleus - physiology ; Cervix Uteri - cytology ; Chains ; complete contour inference ; contour-seed pairs learning ; Contours ; Cytology ; Datasets ; Distance learning ; Drosophila - cytology ; Erythrocytes ; Female ; Fruit flies ; Gene expression ; Humans ; Image Processing, Computer-Assisted - methods ; Image segmentation ; Inference ; Interference ; Kidneys ; Machine Learning ; Medical imaging ; Microscopy ; Microscopy - methods ; Microscopy image segmentation ; Nuclei (cytology) ; Regression analysis ; Regression models ; Robustness ; Shape ; Splines (mathematics)</subject><ispartof>IEEE transactions on image processing, 2018-12, Vol.27 (12), p.5759-5774</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-a32f47040ed7dfa828ca650749da4b34d458b902158caa0b73268cef651fd2693</citedby><cites>FETCH-LOGICAL-c347t-a32f47040ed7dfa828ca650749da4b34d458b902158caa0b73268cef651fd2693</cites><orcidid>0000-0003-0178-9384</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8412504$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8412504$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30028701$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Song, Jie</creatorcontrib><creatorcontrib>Xiao, Liang</creatorcontrib><creatorcontrib>Lian, Zhichao</creatorcontrib><title>Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset.</description><subject>Active contours</subject><subject>Algorithms</subject><subject>Animals</subject><subject>Automation</subject><subject>Boundaries</subject><subject>cascade sparse regression chain model</subject><subject>Cell Line</subject><subject>Cell Nucleus - physiology</subject><subject>Cervix Uteri - cytology</subject><subject>Chains</subject><subject>complete contour inference</subject><subject>contour-seed pairs learning</subject><subject>Contours</subject><subject>Cytology</subject><subject>Datasets</subject><subject>Distance learning</subject><subject>Drosophila - cytology</subject><subject>Erythrocytes</subject><subject>Female</subject><subject>Fruit flies</subject><subject>Gene expression</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Inference</subject><subject>Interference</subject><subject>Kidneys</subject><subject>Machine Learning</subject><subject>Medical imaging</subject><subject>Microscopy</subject><subject>Microscopy - methods</subject><subject>Microscopy image segmentation</subject><subject>Nuclei (cytology)</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Robustness</subject><subject>Shape</subject><subject>Splines (mathematics)</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkU9v1DAQxS1ERUvhjoSELHHhku2M7cTJEbaUrrTQSlu4Wt5ksnLJv9pJ0X4IvjMOu_TAyfbMb57e-DH2BmGBCMXF3ep2IQDzhchTDYDP2BkWChMAJZ7HO6Q60aiKU_YyhPsIqBSzF-xUAohcA56x38u-G_vJJxuiit9a5wNfk_Wd63bJJxti8crbln71_ieve883rp2a0XbUT6HZ80saqRwjzG1X8Q3tWur-Pn9Y7yLCbx7JN3YY5tqSmiZcfJvKhhx3Hf_qSt-Hsh_2fNXaHYVX7KS2TaDXx_Ocfb_6fLe8TtY3X1bLj-uklEqPiZWiVhoUUKWr2uYiL22WglZFZdVWqkql-bYAgWlsWNhqKbK8pDpLsa5EVshz9uGgO_j-YaIwmtaFMro77GUEaCmFQJFG9P1_6H38ri66MwJRY4aFmAXhQM0LBU-1Gbxrrd8bBDNHZWJUZo7KHKOKI--OwtO2pepp4F82EXh7ABwRPbVzFV2Bkn8AO1KYpA</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Song, Jie</creator><creator>Xiao, Liang</creator><creator>Lian, Zhichao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0178-9384</orcidid></search><sort><creationdate>20181201</creationdate><title>Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images</title><author>Song, Jie ; Xiao, Liang ; Lian, Zhichao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-a32f47040ed7dfa828ca650749da4b34d458b902158caa0b73268cef651fd2693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Active contours</topic><topic>Algorithms</topic><topic>Animals</topic><topic>Automation</topic><topic>Boundaries</topic><topic>cascade sparse regression chain model</topic><topic>Cell Line</topic><topic>Cell Nucleus - physiology</topic><topic>Cervix Uteri - cytology</topic><topic>Chains</topic><topic>complete contour inference</topic><topic>contour-seed pairs learning</topic><topic>Contours</topic><topic>Cytology</topic><topic>Datasets</topic><topic>Distance learning</topic><topic>Drosophila - cytology</topic><topic>Erythrocytes</topic><topic>Female</topic><topic>Fruit flies</topic><topic>Gene expression</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image segmentation</topic><topic>Inference</topic><topic>Interference</topic><topic>Kidneys</topic><topic>Machine Learning</topic><topic>Medical imaging</topic><topic>Microscopy</topic><topic>Microscopy - methods</topic><topic>Microscopy image segmentation</topic><topic>Nuclei (cytology)</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Robustness</topic><topic>Shape</topic><topic>Splines (mathematics)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Jie</creatorcontrib><creatorcontrib>Xiao, Liang</creatorcontrib><creatorcontrib>Lian, Zhichao</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Song, Jie</au><au>Xiao, Liang</au><au>Lian, Zhichao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2018-12-01</date><risdate>2018</risdate><volume>27</volume><issue>12</issue><spage>5759</spage><epage>5774</epage><pages>5759-5774</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30028701</pmid><doi>10.1109/TIP.2018.2857001</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-0178-9384</orcidid></addata></record> |
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subjects | Active contours Algorithms Animals Automation Boundaries cascade sparse regression chain model Cell Line Cell Nucleus - physiology Cervix Uteri - cytology Chains complete contour inference contour-seed pairs learning Contours Cytology Datasets Distance learning Drosophila - cytology Erythrocytes Female Fruit flies Gene expression Humans Image Processing, Computer-Assisted - methods Image segmentation Inference Interference Kidneys Machine Learning Medical imaging Microscopy Microscopy - methods Microscopy image segmentation Nuclei (cytology) Regression analysis Regression models Robustness Shape Splines (mathematics) |
title | Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images |
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