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<meta name="DC.Title" content="Automated White Matter Total Lesion Volume Segmentation in Diabetes" />
<meta name="DC.Identifier" content="10.3174/ajnr.A3590" />
<meta name="DC.Date" content="2013-12-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2013 by American Journal of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org" />
<meta name="DC.AccessRights" content="open-access" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes.

MATERIALS AND METHODS: Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods.

RESULTS: Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden.

CONCLUSIONS: We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.

DM
:   type 2 diabetes mellitus
LST
:   Lesion Segmentation Toolbox
SPM8
:   Statistical Parametric Mapping
TLV
:   total lesion volume
GT
:   ground-truth" />
<meta name="DC.Contributor" content="J.A. Maldjian" />
<meta name="DC.Contributor" content="C.T. Whitlow" />
<meta name="DC.Contributor" content="B.N. Saha" />
<meta name="DC.Contributor" content="G. Kota" />
<meta name="DC.Contributor" content="C. Vandergriff" />
<meta name="DC.Contributor" content="E.M. Davenport" />
<meta name="DC.Contributor" content="J. Divers" />
<meta name="DC.Contributor" content="B.I. Freedman" />
<meta name="DC.Contributor" content="D.W. Bowden" />
<meta name="article:published_time" content="2013-12-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="Automated White Matter Total Lesion Volume Segmentation in Diabetes" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of &lt;i&gt;k&lt;/i&gt; = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at &lt;i&gt;k&lt;/i&gt; = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.&lt;/p&gt;" />
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<meta name="twitter:title" content="Automated White Matter Total Lesion Volume Segmentation in Diabetes" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/34/12.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes.

MATERIALS AND METHODS: Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods.

RESULTS: Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden.

CONCLUSIONS: We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.

DM
:   type 2 diabetes mellitus
LST
:   Lesion Segmentation Toolbox
SPM8
:   Statistical Parametric Mapping
TLV
:   total lesion volume
GT
:   ground-truth" />
<meta name="og-title" property="og:title" content="Automated White Matter Total Lesion Volume Segmentation in Diabetes" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/34/12/2265" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes.

MATERIALS AND METHODS: Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods.

RESULTS: Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden.

CONCLUSIONS: We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.

DM
:   type 2 diabetes mellitus
LST
:   Lesion Segmentation Toolbox
SPM8
:   Statistical Parametric Mapping
TLV
:   total lesion volume
GT
:   ground-truth" />
<meta name="og-type" property="og:type" content="article" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node12731--5" data-node-nid="12731" id="top-node-12731--6499390137" data-pisa="ajnr;34/12/2265" data-pisa-master="ajnr;ajnr.A3590" data-apath="/ajnr/34/12/2265.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">Brain</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Automated White Matter Total Lesion Volume Segmentation in Diabetes</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">J.A. Maldjian</span>, <span class="highwire-citation-author" data-delta="1">C.T. Whitlow</span>, <span class="highwire-citation-author" data-delta="2">B.N. Saha</span>, <span class="highwire-citation-author" data-delta="3">G. Kota</span>, <span class="highwire-citation-author" data-delta="4">C. Vandergriff</span>, <span class="highwire-citation-author" data-delta="5">E.M. Davenport</span>, <span class="highwire-citation-author" data-delta="6">J. Divers</span>, <span class="highwire-citation-author" data-delta="7">B.I. Freedman</span> and <span class="highwire-citation-author" data-delta="8">D.W. Bowden</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">December 2013,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">34 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2265-2270; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A3590 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-12731--6499390137" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">J.A. Maldjian </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From Advanced Neuroscience Imaging Research (ANSIR) Laboratory (J.A.M., C.T.W., B.N.S., G.K., C.V., E.M.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A.M., C.T.W., B.N.S., G.K., C.V.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=J.A.%2BMaldjian%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Maldjian%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AJ.A.%2BMaldjian%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">C.T. Whitlow </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From Advanced Neuroscience Imaging Research (ANSIR) Laboratory (J.A.M., C.T.W., B.N.S., G.K., C.V., E.M.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A.M., C.T.W., B.N.S., G.K., C.V.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Translational Science Institute (C.T.W., B.N.S.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=C.T.%2BWhitlow%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Whitlow%20C&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AC.T.%2BWhitlow%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">B.N. Saha </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From Advanced Neuroscience Imaging Research (ANSIR) Laboratory (J.A.M., C.T.W., B.N.S., G.K., C.V., E.M.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A.M., C.T.W., B.N.S., G.K., C.V.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Translational Science Institute (C.T.W., B.N.S.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=B.N.%2BSaha%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Saha%20B&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AB.N.%2BSaha%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">G. Kota </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From Advanced Neuroscience Imaging Research (ANSIR) Laboratory (J.A.M., C.T.W., B.N.S., G.K., C.V., E.M.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A.M., C.T.W., B.N.S., G.K., C.V.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=G.%2BKota%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Kota%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AG.%2BKota%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">C. Vandergriff </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From Advanced Neuroscience Imaging Research (ANSIR) Laboratory (J.A.M., C.T.W., B.N.S., G.K., C.V., E.M.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A.M., C.T.W., B.N.S., G.K., C.V.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=C.%2BVandergriff%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Vandergriff%20C&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AC.%2BVandergriff%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">E.M. Davenport </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From Advanced Neuroscience Imaging Research (ANSIR) Laboratory (J.A.M., C.T.W., B.N.S., G.K., C.V., E.M.D.)</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Biomedical Engineering (E.M.D.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=E.M.%2BDavenport%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Davenport%20E&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AE.M.%2BDavenport%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">J. Divers </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Public Health Sciences (J.D.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=J.%2BDivers%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Divers%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AJ.%2BDivers%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">B.I. Freedman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>Department of Internal Medicine, Section on Nephrology (B.I.F.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=B.I.%2BFreedman%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Freedman%20B&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AB.I.%2BFreedman%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">D.W. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" xmlns:xhtml="http://www.w3.org/1999/xhtml" class="section ref-list" id="ref-list-1"><h2>REFERENCES</h2><ol class="cit-list ref-use-labels"><li><span class="ref-label">1.</span><a class="rev-xref-ref" href="#xref-ref-1-1" title="View reference 1. in text" id="ref-1">↵</a>

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<div class="cit ref-cit ref-journal" id="cit-34.12.2265.26" data-doi="10.2337/dc06-1637"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">van Harten</span>  <span class="cit-name-given-names">B</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">de Leeuw</span>  <span class="cit-name-given-names">FE</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Weinstein</span>  <span class="cit-name-given-names">HC</span></span>, </li><li><span class="cit-etal">et al</span></li></ol><cite>. <span class="cit-article-title">Brain imaging in patients with diabetes: a systematic review</span>. <abbr class="cit-jnl-abbrev">Diabetes Care</abbr> <span class="cit-pub-date">2006</span>;<span class="cit-vol">29</span>:<span class="cit-fpage">2539</span>–<span class="cit-lpage">48</span></cite></div><div class="cit-extra"><a href="/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiRlVMTCI7czoxMToiam91cm5hbENvZGUiO3M6NzoiZGlhY2FyZSI7czo1OiJyZXNpZCI7czoxMDoiMjkvMTEvMjUzOSI7czo0OiJhdG9tIjtzOjIxOiIvYWpuci8zNC8xMi8yMjY1LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==" class="cit-ref-sprinkles cit-ref-sprinkles-ijlink"><span><span class="cit-reflinks-full-text"><span class="free-full-text">FREE </span>Full Text</span></span></a></div></div></li><li><span class="ref-label">27.</span><a class="rev-xref-ref" href="#xref-ref-27-1" title="View reference 27. in text" id="ref-27">↵</a>

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<div class="cit ref-cit ref-journal" id="cit-34.12.2265.28" data-doi="10.1016/j.neuroimage.2011.09.041"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Hsu</span>  <span class="cit-name-given-names">JL</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Chen</span>  <span class="cit-name-given-names">YL</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Leu</span>  <span class="cit-name-given-names">JG</span></span>, </li><li><span class="cit-etal">et al</span></li></ol><cite>. <span class="cit-article-title">Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study</span>. <abbr class="cit-jnl-abbrev">Neuroimage</abbr> <span class="cit-pub-date">2012</span>;<span class="cit-vol">59</span>:<span class="cit-fpage">1098</span>–<span class="cit-lpage">105</span></cite></div><div class="cit-extra"><a href="/lookup/external-ref?access_num=10.1016/j.neuroimage.2011.09.041&amp;link_type=DOI" class="cit-ref-sprinkles cit-ref-sprinkles-doi cit-ref-sprinkles-crossref"><span>CrossRef</span></a><a href="/lookup/external-ref?access_num=21967726&amp;link_type=MED&amp;atom=%2Fajnr%2F34%2F12%2F2265.atom" class="cit-ref-sprinkles cit-ref-sprinkles-medline"><span>PubMed</span></a><a href="/lookup/external-ref?access_num=000298210600030&amp;link_type=ISI" class="cit-ref-sprinkles cit-ref-sprinkles-newisilink cit-ref-sprinkles-webofscience"><span>Web of Science</span></a></div></div></li></ol></div></div>  </div>

  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 34</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 12 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Dec 2013 </div></div>
      
      
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      <div class="highwire-cite-title">Automated White Matter Total Lesion Volume Segmentation in Diabetes</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">J.A.</span> <span class="nlm-surname">Maldjian</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.T.</span> <span class="nlm-surname">Whitlow</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">B.N.</span> <span class="nlm-surname">Saha</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Kota</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Vandergriff</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">E.M.</span> <span class="nlm-surname">Davenport</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Divers</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">B.I.</span> <span class="nlm-surname">Freedman</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">D.W.</span> <span class="nlm-surname">Bowden</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2013, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">34 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2265-2270; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A3590 </span></div>
  
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">J.A.</span> <span class="nlm-surname">Maldjian</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.T.</span> <span class="nlm-surname">Whitlow</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">B.N.</span> <span class="nlm-surname">Saha</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Kota</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Vandergriff</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">E.M.</span> <span class="nlm-surname">Davenport</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Divers</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">B.I.</span> <span class="nlm-surname">Freedman</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">D.W.</span> <span class="nlm-surname">Bowden</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2013, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">34 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2265-2270; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A3590 </span></div>
  
  
  
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