An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images
Summary With the rapid advance of three‐dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in t...
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Veröffentlicht in: | Journal of microscopy (Oxford) 2011-07, Vol.243 (1), p.60-76 |
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description | Summary
With the rapid advance of three‐dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images. |
doi_str_mv | 10.1111/j.1365-2818.2010.03482.x |
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With the rapid advance of three‐dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images.</description><identifier>ISSN: 0022-2720</identifier><identifier>EISSN: 1365-2818</identifier><identifier>DOI: 10.1111/j.1365-2818.2010.03482.x</identifier><identifier>PMID: 21288236</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Animals ; Automation - methods ; Brain - pathology ; Cell clustering ; Cellular ; cluster splitting ; Clusters ; concave points ; Imaging, Three-Dimensional - methods ; Mice ; Microscopy, Confocal - methods ; Segmentation ; split path ; Splitting ; Three dimensional ; Two dimensional</subject><ispartof>Journal of microscopy (Oxford), 2011-07, Vol.243 (1), p.60-76</ispartof><rights>2011 Nanyang Technological University Journal of Microscopy © 2011 Royal Microscopical Society</rights><rights>2011 Nanyang Technological University Journal of Microscopy © 2011 Royal Microscopical Society.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4172-c9c7081acc0dbf732f63ba271802c660428e7ccac56c00152160715e42e907633</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1365-2818.2010.03482.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1365-2818.2010.03482.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21288236$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>INDHUMATHI, C.</creatorcontrib><creatorcontrib>CAI, Y.Y.</creatorcontrib><creatorcontrib>GUAN, Y.Q.</creatorcontrib><creatorcontrib>OPAS, M.</creatorcontrib><title>An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images</title><title>Journal of microscopy (Oxford)</title><addtitle>J Microsc</addtitle><description>Summary
With the rapid advance of three‐dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Automation - methods</subject><subject>Brain - pathology</subject><subject>Cell clustering</subject><subject>Cellular</subject><subject>cluster splitting</subject><subject>Clusters</subject><subject>concave points</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Mice</subject><subject>Microscopy, Confocal - methods</subject><subject>Segmentation</subject><subject>split path</subject><subject>Splitting</subject><subject>Three dimensional</subject><subject>Two dimensional</subject><issn>0022-2720</issn><issn>1365-2818</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1PxCAQQInR6Lr6Fww3T12HoQV68GD8NhoveiYs0rUbWtZC1f33tq56lcMwmXmZwDxCKIMZG87Jcsa4KDJUTM0QhirwXOHsc4tM_hrbZAKAmKFE2CP7MS4BQBUKdskeMlQKuZiQ-VlLTZ9CY1JtaXSLxrVpyMNQ9ovQ1em1oVXoKL-g1nlPre9jch2NK1-nVLcL2scxvgffNy51wxQb2ipY42ndmIWLB2SnMj66w597Sp6vLp_Ob7L7x-vb87P7zOZMYmZLK0ExYy28zCvJsRJ8blAyBWiFgByVk9YaWwgLwApkAiQrXI6uBCk4n5LjzdxVF956F5Nu6ji-2bQu9FGrUiCXIMv_SclKIQuRD-TRD9nPG_eiV93wp26tfxc4AKcb4KP2bv3XZ6BHUXqpRx969KFHUfpblP7Udw-3Y8a_ALWVhis</recordid><startdate>201107</startdate><enddate>201107</enddate><creator>INDHUMATHI, C.</creator><creator>CAI, Y.Y.</creator><creator>GUAN, Y.Q.</creator><creator>OPAS, M.</creator><general>Blackwell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>201107</creationdate><title>An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images</title><author>INDHUMATHI, C. ; CAI, Y.Y. ; GUAN, Y.Q. ; OPAS, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4172-c9c7081acc0dbf732f63ba271802c660428e7ccac56c00152160715e42e907633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Automation - methods</topic><topic>Brain - pathology</topic><topic>Cell clustering</topic><topic>Cellular</topic><topic>cluster splitting</topic><topic>Clusters</topic><topic>concave points</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Mice</topic><topic>Microscopy, Confocal - methods</topic><topic>Segmentation</topic><topic>split path</topic><topic>Splitting</topic><topic>Three dimensional</topic><topic>Two dimensional</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>INDHUMATHI, C.</creatorcontrib><creatorcontrib>CAI, Y.Y.</creatorcontrib><creatorcontrib>GUAN, Y.Q.</creatorcontrib><creatorcontrib>OPAS, M.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of microscopy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>INDHUMATHI, C.</au><au>CAI, Y.Y.</au><au>GUAN, Y.Q.</au><au>OPAS, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images</atitle><jtitle>Journal of microscopy (Oxford)</jtitle><addtitle>J Microsc</addtitle><date>2011-07</date><risdate>2011</risdate><volume>243</volume><issue>1</issue><spage>60</spage><epage>76</epage><pages>60-76</pages><issn>0022-2720</issn><eissn>1365-2818</eissn><abstract>Summary
With the rapid advance of three‐dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>21288236</pmid><doi>10.1111/j.1365-2818.2010.03482.x</doi><tpages>17</tpages></addata></record> |
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subjects | Algorithms Animals Automation - methods Brain - pathology Cell clustering Cellular cluster splitting Clusters concave points Imaging, Three-Dimensional - methods Mice Microscopy, Confocal - methods Segmentation split path Splitting Three dimensional Two dimensional |
title | An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images |
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