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<meta name="DC.Title" content="Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8046" />
<meta name="DC.Date" content="2023-12-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2023 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: The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls.

MATERIALS AND METHODS: We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria.

RESULTS: In control fetuses, all parameters changed significantly with gestational age ( P &lt; .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters ( P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters ( P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses ( n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses ( n = 33), with an area under the curve of 0.84 and a recall of 0.62.

CONCLUSIONS: This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.

AUC
:   area under the curve
GA
:   gestational age
GI
:   gyrification index
GIA
:   gyrification index by area
GIC
:   gyrification index by contour
LIS
:   lissencephaly
MCC
:   Matthews correlation coefficient
max
:   maximum
MCD
:   malformation of cortical development
PMG
:   polymicrogyria
SI
:   symmetry index" />
<meta name="DC.Contributor" content="Bossmat Yehuda" />
<meta name="DC.Contributor" content="Aviad Rabinowich" />
<meta name="DC.Contributor" content="Daphna Link-Sourani" />
<meta name="DC.Contributor" content="Netanell Avisdris" />
<meta name="DC.Contributor" content="Ori Ben-Zvi" />
<meta name="DC.Contributor" content="Bella Specktor-Fadida" />
<meta name="DC.Contributor" content="Leo Joskowicz" />
<meta name="DC.Contributor" content="Liat Ben-Sira" />
<meta name="DC.Contributor" content="Elka Miller" />
<meta name="DC.Contributor" content="Dafna Ben Bashat" />
<meta name="article:published_time" content="2023-12-01" />
<meta name="article:section" content="Pediatric Neuroimaging" />
<meta name="citation_title" content="Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;In control fetuses, all parameters changed significantly with gestational age (&lt;i&gt;P&lt;/i&gt; &amp;lt; .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters (&lt;i&gt;P&lt;/i&gt; ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters (&lt;i&gt;P&lt;/i&gt; ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses (&lt;i&gt;n&lt;/i&gt; = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses (&lt;i&gt;n&lt;/i&gt; = 33), with an area under the curve of 0.84 and a recall of 0.62.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_publication_date" content="2023/12/01" />
<meta name="citation_mjid" content="ajnr;44/12/1432" />
<meta name="citation_id" content="44/12/1432" />
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<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A8046" />
<meta name="citation_pmid" content="38050002" />
<meta name="citation_volume" content="44" />
<meta name="citation_issue" content="12" />
<meta name="citation_article_type" content="Research Article" />
<meta name="citation_section" content="Pediatric Neuroimaging" />
<meta name="citation_firstpage" content="1432" />
<meta name="citation_lastpage" content="1439" />
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<meta name="citation_author" content="Bossmat Yehuda" />
<meta name="citation_author_institution" content="aFrom the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel" />
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<meta name="twitter:title" content="Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/44/12.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls.

MATERIALS AND METHODS: We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria.

RESULTS: In control fetuses, all parameters changed significantly with gestational age ( P &lt; .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters ( P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters ( P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses ( n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses ( n = 33), with an area under the curve of 0.84 and a recall of 0.62.

CONCLUSIONS: This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.

AUC
:   area under the curve
GA
:   gestational age
GI
:   gyrification index
GIA
:   gyrification index by area
GIC
:   gyrification index by contour
LIS
:   lissencephaly
MCC
:   Matthews correlation coefficient
max
:   maximum
MCD
:   malformation of cortical development
PMG
:   polymicrogyria
SI
:   symmetry index" />
<meta name="og-title" property="og:title" content="Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/44/12/1432" />
<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: The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls.

MATERIALS AND METHODS: We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria.

RESULTS: In control fetuses, all parameters changed significantly with gestational age ( P &lt; .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters ( P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters ( P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses ( n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses ( n = 33), with an area under the curve of 0.84 and a recall of 0.62.

CONCLUSIONS: This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.

AUC
:   area under the curve
GA
:   gestational age
GI
:   gyrification index
GIA
:   gyrification index by area
GIC
:   gyrification index by contour
LIS
:   lissencephaly
MCC
:   Matthews correlation coefficient
max
:   maximum
MCD
:   malformation of cortical development
PMG
:   polymicrogyria
SI
:   symmetry index" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node105025--5" data-node-nid="105025" id="top-node-105025--6138545972" data-pisa="ajnr;44/12/1432" data-pisa-master="ajnr;ajnr.A8046" data-apath="/ajnr/44/12/1432.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">Pediatric Neuroimaging</span></span></div>
  
      <div class="highwire-cite-access"><span class="highwire-citation-access"><i class="highwire-access-icon highwire-access-icon-open-access open-access hw-icon-open-access" title="Open Access" aria-hidden="true"></i><span  class="element-invisible highwire-access-icon highwire-access-icon-open-access" style="">Open Access</span></span></div>
  
      <h1 class="highwire-cite-title" id="page-title">Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Bossmat Yehuda</span>, <span class="highwire-citation-author" data-delta="1">Aviad Rabinowich</span>, <span class="highwire-citation-author" data-delta="2">Daphna Link-Sourani</span>, <span class="highwire-citation-author" data-delta="3">Netanell Avisdris</span>, <span class="highwire-citation-author" data-delta="4">Ori Ben-Zvi</span>, <span class="highwire-citation-author" data-delta="5">Bella Specktor-Fadida</span>, <span class="highwire-citation-author" data-delta="6">Leo Joskowicz</span>, <span class="highwire-citation-author" data-delta="7">Liat Ben-Sira</span>, <span class="highwire-citation-author" data-delta="8">Elka Miller</span> and <span class="highwire-citation-author" data-delta="9">Dafna Ben Bashat</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 2023,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1432-1439; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8046 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-105025--6138545972" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Bossmat Yehuda </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Sagol School of Neuroscience (B.Y., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel</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=Bossmat%2BYehuda%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=Yehuda%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"><a href="/search/author1%3ABossmat%2BYehuda%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-5506-1498" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Bossmat Yehuda</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Aviad Rabinowich </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Sackler Faculty of Medicine (A.R., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Division of Radiology (A.R., L.B.-S.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</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=Aviad%2BRabinowich%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=Rabinowich%20A&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"><a href="/search/author1%3AAviad%2BRabinowich%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-9406-6523" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Aviad Rabinowich</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Daphna Link-Sourani </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</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=Daphna%2BLink-Sourani%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=Link-Sourani%20D&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"><a href="/search/author1%3ADaphna%2BLink-Sourani%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0003-2422-6795" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Daphna Link-Sourani</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Netanell Avisdris </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>d</span>School of Computer Science and Engineering (N.A., L.J.), The Hebrew University of Jerusalem, Jerusalem, Israel</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=Netanell%2BAvisdris%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=Avisdris%20N&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"><a href="/search/author1%3ANetanell%2BAvisdris%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-0719-1327" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Netanell Avisdris</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Ori Ben-Zvi </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</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=Ori%2BBen-Zvi%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=Ben-Zvi%20O&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li 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href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Leo%2BJoskowicz%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=Joskowicz%20L&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"><a href="/search/author1%3ALeo%2BJoskowicz%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-3010-4770" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Leo Joskowicz</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">Liat Ben-Sira </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Sagol School of Neuroscience (B.Y., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Sackler Faculty of Medicine (A.R., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Division of Radiology (A.R., L.B.-S.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</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=Liat%2BBen-Sira%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=Ben-Sira%20L&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" 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href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Elka%2BMiller%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=Miller%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"><a href="/search/author1%3AElka%2BMiller%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0003-1075-5952" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Elka Miller</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">Dafna Ben Bashat </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Sagol School of Neuroscience (B.Y., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Sackler Faculty of Medicine (A.R., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel</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=Dafna%2BBen%2BBashat%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=Ben%20Bashat%20D&amp;link_type=AUTHORSEARCH" 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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">Bossmat</span> <span class="nlm-surname">Yehuda</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Aviad</span> <span class="nlm-surname">Rabinowich</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Daphna</span> <span class="nlm-surname">Link-Sourani</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Netanell</span> <span class="nlm-surname">Avisdris</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Ori</span> <span class="nlm-surname">Ben-Zvi</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Bella</span> <span class="nlm-surname">Specktor-Fadida</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Leo</span> <span class="nlm-surname">Joskowicz</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Liat</span> <span class="nlm-surname">Ben-Sira</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Elka</span> <span class="nlm-surname">Miller</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Dafna</span> <span class="nlm-surname">Ben Bashat</span></span></span></div>
  
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      <div class="highwire-cite-title">Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI</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">Bossmat</span> <span class="nlm-surname">Yehuda</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Aviad</span> <span class="nlm-surname">Rabinowich</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Daphna</span> <span class="nlm-surname">Link-Sourani</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Netanell</span> <span class="nlm-surname">Avisdris</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Ori</span> <span class="nlm-surname">Ben-Zvi</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Bella</span> <span class="nlm-surname">Specktor-Fadida</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Leo</span> <span class="nlm-surname">Joskowicz</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Liat</span> <span class="nlm-surname">Ben-Sira</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Elka</span> <span class="nlm-surname">Miller</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Dafna</span> <span class="nlm-surname">Ben Bashat</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 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1432-1439; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8046 </span></div>
  
  
  
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