Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications
Identifieur interne : 001D67 ( Istex/Corpus ); précédent : 001D66; suivant : 001D68Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications
Auteurs : M. Mallar Chakravarty ; Abbas F. Sadikot ; Jürgen Germann ; Pierre Hellier ; Gilles Bertrand ; D. Louis CollinsSource :
- Human Brain Mapping [ 1065-9471 ] ; 2009-11.
English descriptors
- KwdEn :
Abstract
Digital atlases are commonly used in pre‐operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas‐to‐patient warping procedures. Once fitted to pre‐operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece‐wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas‐to‐patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas‐based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece‐wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas‐to‐patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece‐wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.
Url:
DOI: 10.1002/hbm.20780
Links to Exploration step
ISTEX:3B7C392A307416A1F982BE55542A2E466BD98FCFLe document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title><author><name sortKey="Chakravarty, M Mallar" sort="Chakravarty, M Mallar" uniqKey="Chakravarty M" first="M. Mallar" last="Chakravarty">M. Mallar Chakravarty</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Sadikot, Abbas F" sort="Sadikot, Abbas F" uniqKey="Sadikot A" first="Abbas F." last="Sadikot">Abbas F. Sadikot</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Germann, Jurgen" sort="Germann, Jurgen" uniqKey="Germann J" first="Jürgen" last="Germann">Jürgen Germann</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Hellier, Pierre" sort="Hellier, Pierre" uniqKey="Hellier P" first="Pierre" last="Hellier">Pierre Hellier</name><affiliation><mods:affiliation>INRIA, IRISA Visages team, Campus de Beaulieu, Rennes Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>INSERM, Visages‐U746, IRISA, Campus de Beaulieu, Rennes Cedex, France</mods:affiliation></affiliation></author><author><name sortKey="Bertrand, Gilles" sort="Bertrand, Gilles" uniqKey="Bertrand G" first="Gilles" last="Bertrand">Gilles Bertrand</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Collins, D Louis" sort="Collins, D Louis" uniqKey="Collins D" first="D. Louis" last="Collins">D. Louis Collins</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:3B7C392A307416A1F982BE55542A2E466BD98FCF</idno><date when="2009" year="2009">2009</date><idno type="doi">10.1002/hbm.20780</idno><idno type="url">https://api-v5.istex.fr/document/3B7C392A307416A1F982BE55542A2E466BD98FCF/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001D67</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001D67</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title><author><name sortKey="Chakravarty, M Mallar" sort="Chakravarty, M Mallar" uniqKey="Chakravarty M" first="M. Mallar" last="Chakravarty">M. Mallar Chakravarty</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Sadikot, Abbas F" sort="Sadikot, Abbas F" uniqKey="Sadikot A" first="Abbas F." last="Sadikot">Abbas F. Sadikot</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Germann, Jurgen" sort="Germann, Jurgen" uniqKey="Germann J" first="Jürgen" last="Germann">Jürgen Germann</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Hellier, Pierre" sort="Hellier, Pierre" uniqKey="Hellier P" first="Pierre" last="Hellier">Pierre Hellier</name><affiliation><mods:affiliation>INRIA, IRISA Visages team, Campus de Beaulieu, Rennes Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>INSERM, Visages‐U746, IRISA, Campus de Beaulieu, Rennes Cedex, France</mods:affiliation></affiliation></author><author><name sortKey="Bertrand, Gilles" sort="Bertrand, Gilles" uniqKey="Bertrand G" first="Gilles" last="Bertrand">Gilles Bertrand</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author><author><name sortKey="Collins, D Louis" sort="Collins, D Louis" uniqKey="Collins D" first="D. Louis" last="Collins">D. Louis Collins</name><affiliation><mods:affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Human Brain Mapping</title><title level="j" type="abbrev">Hum. Brain Mapp.</title><idno type="ISSN">1065-9471</idno><idno type="eISSN">1097-0193</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2009-11">2009-11</date><biblScope unit="volume">30</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="3574">3574</biblScope><biblScope unit="page" to="3595">3595</biblScope></imprint><idno type="ISSN">1065-9471</idno></series><idno type="istex">3B7C392A307416A1F982BE55542A2E466BD98FCF</idno><idno type="DOI">10.1002/hbm.20780</idno><idno type="ArticleID">HBM20780</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1065-9471</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Parkinson's disease</term><term>atlas customization</term><term>deep brain stimulation (DBS)</term><term>movement disorder</term><term>pallidotomy</term><term>surgical planning</term><term>thalamotomy</term><term>warping</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Digital atlases are commonly used in pre‐operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas‐to‐patient warping procedures. Once fitted to pre‐operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece‐wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas‐to‐patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas‐based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece‐wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas‐to‐patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece‐wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>M. Mallar Chakravarty</name><affiliations><json:string>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</json:string><json:string>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</json:string></affiliations></json:item><json:item><name>Abbas F. Sadikot</name><affiliations><json:string>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</json:string><json:string>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</json:string></affiliations></json:item><json:item><name>Jürgen Germann</name><affiliations><json:string>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</json:string></affiliations></json:item><json:item><name>Pierre Hellier</name><affiliations><json:string>INRIA, IRISA Visages team, Campus de Beaulieu, Rennes Cedex, France</json:string><json:string>INSERM, Visages‐U746, IRISA, Campus de Beaulieu, Rennes Cedex, France</json:string></affiliations></json:item><json:item><name>Gilles Bertrand</name><affiliations><json:string>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</json:string><json:string>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</json:string></affiliations></json:item><json:item><name>D. Louis Collins</name><affiliations><json:string>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>atlas customization</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>movement disorder</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>warping</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Parkinson's disease</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>surgical planning</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>thalamotomy</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>pallidotomy</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>deep brain stimulation (DBS)</value></json:item></subject><articleId><json:string>HBM20780</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Digital atlases are commonly used in pre‐operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas‐to‐patient warping procedures. Once fitted to pre‐operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece‐wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas‐to‐patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas‐based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece‐wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas‐to‐patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece‐wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.</abstract><qualityIndicators><score>8</score><pdfVersion>1.3</pdfVersion><pdfPageSize>612 x 810 pts</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>1908</abstractCharCount><pdfWordCount>12580</pdfWordCount><pdfCharCount>80650</pdfCharCount><pdfPageCount>22</pdfPageCount><abstractWordCount>259</abstractWordCount></qualityIndicators><title>Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title><genre><json:string>article</json:string></genre><host><title>Human Brain Mapping</title><language><json:string>unknown</json:string></language><doi><json:string>10.1002/(ISSN)1097-0193</json:string></doi><issn><json:string>1065-9471</json:string></issn><eissn><json:string>1097-0193</json:string></eissn><publisherId><json:string>HBM</json:string></publisherId><volume>30</volume><issue>11</issue><pages><first>3574</first><last>3595</last><total>22</total></pages><genre><json:string>journal</json:string></genre><subject><json:item><value>Research Article</value></json:item></subject></host><categories><wos><json:string>science</json:string><json:string>radiology, nuclear medicine & medical imaging</json:string><json:string>neurosciences</json:string><json:string>neuroimaging</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>psychology & cognitive sciences</json:string><json:string>experimental psychology</json:string></scienceMetrix><inist><json:string>sciences appliquees, technologies et medecines</json:string><json:string>sciences biologiques et medicales</json:string><json:string>sciences medicales</json:string></inist></categories><publicationDate>2009</publicationDate><copyrightDate>2009</copyrightDate><doi><json:string>10.1002/hbm.20780</json:string></doi><id>3B7C392A307416A1F982BE55542A2E466BD98FCF</id><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api-v5.istex.fr/document/3B7C392A307416A1F982BE55542A2E466BD98FCF/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api-v5.istex.fr/document/3B7C392A307416A1F982BE55542A2E466BD98FCF/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api-v5.istex.fr/document/3B7C392A307416A1F982BE55542A2E466BD98FCF/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><availability><p>Copyright © 2009 Wiley‐Liss, Inc.</p></availability><date>2009</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title><author xml:id="author-1"><persName><forename type="first">M. Mallar</forename><surname>Chakravarty</surname></persName><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation></author><author xml:id="author-2"><persName><forename type="first">Abbas F.</forename><surname>Sadikot</surname></persName><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</affiliation></author><author xml:id="author-3"><persName><forename type="first">Jürgen</forename><surname>Germann</surname></persName><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation></author><author xml:id="author-4"><persName><forename type="first">Pierre</forename><surname>Hellier</surname></persName><affiliation>INRIA, IRISA Visages team, Campus de Beaulieu, Rennes Cedex, France</affiliation><affiliation>INSERM, Visages‐U746, IRISA, Campus de Beaulieu, Rennes Cedex, France</affiliation></author><author xml:id="author-5"><persName><forename type="first">Gilles</forename><surname>Bertrand</surname></persName><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</affiliation></author><author xml:id="author-6"><persName><forename type="first">D. Louis</forename><surname>Collins</surname></persName><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation></author></analytic><monogr><title level="j">Human Brain Mapping</title><title level="j" type="abbrev">Hum. Brain Mapp.</title><idno type="pISSN">1065-9471</idno><idno type="eISSN">1097-0193</idno><idno type="DOI">10.1002/(ISSN)1097-0193</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2009-11"></date><biblScope unit="volume">30</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="3574">3574</biblScope><biblScope unit="page" to="3595">3595</biblScope></imprint></monogr><idno type="istex">3B7C392A307416A1F982BE55542A2E466BD98FCF</idno><idno type="DOI">10.1002/hbm.20780</idno><idno type="ArticleID">HBM20780</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2009</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Digital atlases are commonly used in pre‐operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas‐to‐patient warping procedures. Once fitted to pre‐operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece‐wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas‐to‐patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas‐based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece‐wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas‐to‐patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece‐wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>keywords</head><item><term>atlas customization</term></item><item><term>movement disorder</term></item><item><term>warping</term></item><item><term>Parkinson's disease</term></item><item><term>surgical planning</term></item><item><term>thalamotomy</term></item><item><term>pallidotomy</term></item><item><term>deep brain stimulation (DBS)</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Research Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2008-04-08">Received</change><change when="2009-02-20">Registration</change><change when="2009-11">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api-v5.istex.fr/document/3B7C392A307416A1F982BE55542A2E466BD98FCF/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Wiley Subscription Services, Inc., A Wiley Company</publisherName><publisherLoc>Hoboken</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1097-0193</doi><issn type="print">1065-9471</issn><issn type="electronic">1097-0193</issn><idGroup><id type="product" value="HBM"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="HUMAN BRAIN MAPPING">Human Brain Mapping</title><title type="short">Hum. Brain Mapp.</title></titleGroup></publicationMeta><publicationMeta level="part" position="110"><doi origin="wiley" registered="yes">10.1002/hbm.v30:11</doi><numberingGroup><numbering type="journalVolume" number="30">30</numbering><numbering type="journalIssue">11</numbering></numberingGroup><coverDate startDate="2009-11">November 2009</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="100" status="forIssue"><doi origin="wiley" registered="yes">10.1002/hbm.20780</doi><idGroup><id type="unit" value="HBM20780"></id></idGroup><countGroup><count type="pageTotal" number="22"></count></countGroup><titleGroup><title type="articleCategory">Research Article</title><title type="tocHeading1">Research Articles</title></titleGroup><copyright ownership="publisher">Copyright © 2009 Wiley‐Liss, Inc.</copyright><eventGroup><event type="manuscriptReceived" date="2008-04-08"></event><event type="manuscriptRevised" date="2009-02-05"></event><event type="manuscriptAccepted" date="2009-02-20"></event><event type="publishedOnlineEarlyUnpaginated" date="2009-04-22"></event><event type="firstOnline" date="2009-04-22"></event><event type="publishedOnlineFinalForm" date="2009-10-19"></event><event type="xmlConverted" agent="Converter:JWSART34_TO_WML3G version:2.3.2 mode:FullText source:FullText result:FullText mathml2tex" date="2010-03-06"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-26"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-24"></event></eventGroup><numberingGroup><numbering type="pageFirst">3574</numbering><numbering type="pageLast">3595</numbering></numberingGroup><correspondenceTo>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:HBM.HBM20780.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="5"></count><count type="tableTotal" number="11"></count><count type="referenceTotal" number="68"></count><count type="wordTotal" number="17394"></count></countGroup><titleGroup><title type="main" xml:lang="en">Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title><title type="short" xml:lang="en">Comparison of Atlas‐to‐Patient Warping Techniques</title></titleGroup><creators><creator xml:id="au1" creatorRole="author" affiliationRef="#af1" corresponding="yes"><personName><givenNames>M. Mallar</givenNames><familyName>Chakravarty</familyName></personName><contactDetails><email>mallar@bic.mni.mcgill.ca</email></contactDetails></creator><creator xml:id="au2" creatorRole="author" affiliationRef="#af1 #af2"><personName><givenNames>Abbas F.</givenNames><familyName>Sadikot</familyName></personName></creator><creator xml:id="au3" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Jürgen</givenNames><familyName>Germann</familyName></personName></creator><creator xml:id="au4" creatorRole="author" affiliationRef="#af3 #af4"><personName><givenNames>Pierre</givenNames><familyName>Hellier</familyName></personName></creator><creator xml:id="au5" creatorRole="author" affiliationRef="#af1 #af2"><personName><givenNames>Gilles</givenNames><familyName>Bertrand</familyName></personName></creator><creator xml:id="au6" creatorRole="author" affiliationRef="#af1"><personName><givenNames>D. Louis</givenNames><familyName>Collins</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="af1" countryCode="CA" type="organization"><unparsedAffiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</unparsedAffiliation></affiliation><affiliation xml:id="af2" countryCode="CA" type="organization"><unparsedAffiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</unparsedAffiliation></affiliation><affiliation xml:id="af3" countryCode="FR" type="organization"><unparsedAffiliation>INRIA, IRISA Visages team, Campus de Beaulieu, Rennes Cedex, France</unparsedAffiliation></affiliation><affiliation xml:id="af4" countryCode="FR" type="organization"><unparsedAffiliation>INSERM, Visages‐U746, IRISA, Campus de Beaulieu, Rennes Cedex, France</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">atlas customization</keyword><keyword xml:id="kwd2">movement disorder</keyword><keyword xml:id="kwd3">warping</keyword><keyword xml:id="kwd4">Parkinson's disease</keyword><keyword xml:id="kwd5">surgical planning</keyword><keyword xml:id="kwd6">thalamotomy</keyword><keyword xml:id="kwd7">pallidotomy</keyword><keyword xml:id="kwd8">deep brain stimulation (DBS)</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Digital atlases are commonly used in pre‐operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas‐to‐patient warping procedures. Once fitted to pre‐operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece‐wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas‐to‐patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas‐based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece‐wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas‐to‐patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece‐wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Comparison of Atlas‐to‐Patient Warping Techniques</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications</title></titleInfo><name type="personal"><namePart type="given">M. Mallar</namePart><namePart type="family">Chakravarty</namePart><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Abbas F.</namePart><namePart type="family">Sadikot</namePart><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jürgen</namePart><namePart type="family">Germann</namePart><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Pierre</namePart><namePart type="family">Hellier</namePart><affiliation>INRIA, IRISA Visages team, Campus de Beaulieu, Rennes Cedex, France</affiliation><affiliation>INSERM, Visages‐U746, IRISA, Campus de Beaulieu, Rennes Cedex, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Gilles</namePart><namePart type="family">Bertrand</namePart><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><affiliation>Division of Neurosurgery, McGill University, Montréal, Quebec, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">D. Louis</namePart><namePart type="family">Collins</namePart><affiliation>McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2009-11</dateIssued><dateCaptured encoding="w3cdtf">2008-04-08</dateCaptured><dateValid encoding="w3cdtf">2009-02-20</dateValid><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">5</extent><extent unit="tables">11</extent><extent unit="references">68</extent><extent unit="words">17394</extent></physicalDescription><abstract lang="en">Digital atlases are commonly used in pre‐operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas‐to‐patient warping procedures. Once fitted to pre‐operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece‐wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas‐to‐patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas‐based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece‐wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas‐to‐patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece‐wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.</abstract><subject lang="en"><genre>keywords</genre><topic>atlas customization</topic><topic>movement disorder</topic><topic>warping</topic><topic>Parkinson's disease</topic><topic>surgical planning</topic><topic>thalamotomy</topic><topic>pallidotomy</topic><topic>deep brain stimulation (DBS)</topic></subject><relatedItem type="host"><titleInfo><title>Human Brain Mapping</title></titleInfo><titleInfo type="abbreviated"><title>Hum. Brain Mapp.</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Research Article</topic></subject><identifier type="ISSN">1065-9471</identifier><identifier type="eISSN">1097-0193</identifier><identifier type="DOI">10.1002/(ISSN)1097-0193</identifier><identifier type="PublisherID">HBM</identifier><part><date>2009</date><detail type="volume"><caption>vol.</caption><number>30</number></detail><detail type="issue"><caption>no.</caption><number>11</number></detail><extent unit="pages"><start>3574</start><end>3595</end><total>22</total></extent></part></relatedItem><identifier type="istex">3B7C392A307416A1F982BE55542A2E466BD98FCF</identifier><identifier type="DOI">10.1002/hbm.20780</identifier><identifier type="ArticleID">HBM20780</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2009 Wiley‐Liss, Inc.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Wiley Subscription Services, Inc., A Wiley Company</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Canada/explor/ParkinsonCanadaV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001D67 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 001D67 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Canada
|area= ParkinsonCanadaV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:3B7C392A307416A1F982BE55542A2E466BD98FCF
|texte= Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications
}}
| This area was generated with Dilib version V0.6.29. |  |