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<meta name="HW.identifier" content="/ajnr/44/6/641.atom" />
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<meta name="DC.Language" content="en" />
<meta name="DC.Title" content="Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7878" />
<meta name="DC.Date" content="2023-06-01" />
<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: Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in patients with stroke.

MATERIALS AND METHODS: A total of 499 patients with large-vessel occlusion from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial were included. All patients had thin-section NCCT and CTA images. Thrombi contoured manually were used as reference standard. A deep learning approach was developed to segment thrombi automatically. Of 499 patients, 263 and 66 patients were randomly selected to train and validate the deep learning model, respectively; the remaining 170 patients were independently used for testing. The deep learning model was quantitatively compared with the reference standard using the Dice coefficient and volumetric error. The proposed deep learning model was externally tested on 83 patients with and without large-vessel occlusion from another independent trial.

RESULTS: The developed deep learning approach obtained a Dice coefficient of 70.7% (interquartile range, 58.0%–77.8%) in the internal cohort. The predicted thrombi length and volume were correlated with those of expert-contoured thrombi ( r = 0.88 and 0.87, respectively; P  &lt; .001). When the derived deep learning model was applied to the external data set, the model obtained similar results in patients with large-vessel occlusion regarding the Dice coefficient (66.8%; interquartile range, 58.5%–74.6%), thrombus length ( r = 0.73), and volume ( r = 0.80). The model also obtained a sensitivity of 94.12% (32/34) and a specificity of 97.96% (48/49) in classifying large-vessel occlusion versus non-large-vessel occlusion.

CONCLUSIONS: The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.

AIS
:   acute ischemic stroke
ASSD
:   average symmetric surface distance
DC
:   Dice coefficient
DL
:   deep learning
EVT
:   endovascular therapy
HD95
:   95th percentile of the Hausdorff distance
IQR
:   interquartile range
LVO
:   large-vessel occlusion
MeVO
:   medium-vessel occlusion
STAPLE
:   simultaneous truth and performance level estimation" />
<meta name="DC.Contributor" content="K. Zhu" />
<meta name="DC.Contributor" content="F. Bala" />
<meta name="DC.Contributor" content="J. Zhang" />
<meta name="DC.Contributor" content="F. Benali" />
<meta name="DC.Contributor" content="P. Cimflova" />
<meta name="DC.Contributor" content="B.J. Kim" />
<meta name="DC.Contributor" content="R. McDonough" />
<meta name="DC.Contributor" content="N. Singh" />
<meta name="DC.Contributor" content="M.D. Hill" />
<meta name="DC.Contributor" content="M. Goyal" />
<meta name="DC.Contributor" content="A. Demchuk" />
<meta name="DC.Contributor" content="B.K. Menon" />
<meta name="DC.Contributor" content="W. Qiu" />
<meta name="article:published_time" content="2023-06-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in patients with stroke.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;A total of 499 patients with large-vessel occlusion from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial were included. All patients had thin-section NCCT and CTA images. Thrombi contoured manually were used as reference standard. A deep learning approach was developed to segment thrombi automatically. Of 499 patients, 263 and 66 patients were randomly selected to train and validate the deep learning model, respectively; the remaining 170 patients were independently used for testing. The deep learning model was quantitatively compared with the reference standard using the Dice coefficient and volumetric error. The proposed deep learning model was externally tested on 83 patients with and without large-vessel occlusion from another independent trial.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The developed deep learning approach obtained a Dice coefficient of 70.7% (interquartile range, 58.0%–77.8%) in the internal cohort. The predicted thrombi length and volume were correlated with those of expert-contoured thrombi (&lt;i&gt;r&lt;/i&gt; = 0.88 and 0.87, respectively; &lt;i&gt;P &lt;/i&gt;&amp;lt; .001). When the derived deep learning model was applied to the external data set, the model obtained similar results in patients with large-vessel occlusion regarding the Dice coefficient (66.8%; interquartile range, 58.5%–74.6%), thrombus length (&lt;i&gt;r&lt;/i&gt; = 0.73), and volume (&lt;i&gt;r&lt;/i&gt; = 0.80). The model also obtained a sensitivity of 94.12% (32/34) and a specificity of 97.96% (48/49) in classifying large-vessel occlusion versus non-large-vessel occlusion.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.&lt;/p&gt;" />
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<meta name="twitter:title" content="Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in patients with stroke.

MATERIALS AND METHODS: A total of 499 patients with large-vessel occlusion from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial were included. All patients had thin-section NCCT and CTA images. Thrombi contoured manually were used as reference standard. A deep learning approach was developed to segment thrombi automatically. Of 499 patients, 263 and 66 patients were randomly selected to train and validate the deep learning model, respectively; the remaining 170 patients were independently used for testing. The deep learning model was quantitatively compared with the reference standard using the Dice coefficient and volumetric error. The proposed deep learning model was externally tested on 83 patients with and without large-vessel occlusion from another independent trial.

RESULTS: The developed deep learning approach obtained a Dice coefficient of 70.7% (interquartile range, 58.0%–77.8%) in the internal cohort. The predicted thrombi length and volume were correlated with those of expert-contoured thrombi ( r = 0.88 and 0.87, respectively; P  &lt; .001). When the derived deep learning model was applied to the external data set, the model obtained similar results in patients with large-vessel occlusion regarding the Dice coefficient (66.8%; interquartile range, 58.5%–74.6%), thrombus length ( r = 0.73), and volume ( r = 0.80). The model also obtained a sensitivity of 94.12% (32/34) and a specificity of 97.96% (48/49) in classifying large-vessel occlusion versus non-large-vessel occlusion.

CONCLUSIONS: The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.

AIS
:   acute ischemic stroke
ASSD
:   average symmetric surface distance
DC
:   Dice coefficient
DL
:   deep learning
EVT
:   endovascular therapy
HD95
:   95th percentile of the Hausdorff distance
IQR
:   interquartile range
LVO
:   large-vessel occlusion
MeVO
:   medium-vessel occlusion
STAPLE
:   simultaneous truth and performance level estimation" />
<meta name="og-title" property="og:title" content="Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/44/6/641" />
<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: Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in patients with stroke.

MATERIALS AND METHODS: A total of 499 patients with large-vessel occlusion from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial were included. All patients had thin-section NCCT and CTA images. Thrombi contoured manually were used as reference standard. A deep learning approach was developed to segment thrombi automatically. Of 499 patients, 263 and 66 patients were randomly selected to train and validate the deep learning model, respectively; the remaining 170 patients were independently used for testing. The deep learning model was quantitatively compared with the reference standard using the Dice coefficient and volumetric error. The proposed deep learning model was externally tested on 83 patients with and without large-vessel occlusion from another independent trial.

RESULTS: The developed deep learning approach obtained a Dice coefficient of 70.7% (interquartile range, 58.0%–77.8%) in the internal cohort. The predicted thrombi length and volume were correlated with those of expert-contoured thrombi ( r = 0.88 and 0.87, respectively; P  &lt; .001). When the derived deep learning model was applied to the external data set, the model obtained similar results in patients with large-vessel occlusion regarding the Dice coefficient (66.8%; interquartile range, 58.5%–74.6%), thrombus length ( r = 0.73), and volume ( r = 0.80). The model also obtained a sensitivity of 94.12% (32/34) and a specificity of 97.96% (48/49) in classifying large-vessel occlusion versus non-large-vessel occlusion.

CONCLUSIONS: The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.

AIS
:   acute ischemic stroke
ASSD
:   average symmetric surface distance
DC
:   Dice coefficient
DL
:   deep learning
EVT
:   endovascular therapy
HD95
:   95th percentile of the Hausdorff distance
IQR
:   interquartile range
LVO
:   large-vessel occlusion
MeVO
:   medium-vessel occlusion
STAPLE
:   simultaneous truth and performance level estimation" />
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      <h1 class="highwire-cite-title" id="page-title">Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">K. Zhu</span>, <span class="highwire-citation-author" data-delta="1">F. Bala</span>, <span class="highwire-citation-author" data-delta="2">J. Zhang</span>, <span class="highwire-citation-author" data-delta="3">F. Benali</span>, <span class="highwire-citation-author" data-delta="4">P. Cimflova</span>, <span class="highwire-citation-author" data-delta="5">B.J. Kim</span>, <span class="highwire-citation-author" data-delta="6">R. McDonough</span>, <span class="highwire-citation-author" data-delta="7">N. Singh</span>, <span class="highwire-citation-author" data-delta="8">M.D. Hill</span>, <span class="highwire-citation-author" data-delta="9">M. Goyal</span>, <span class="highwire-citation-author" data-delta="10">A. Demchuk</span>, <span class="highwire-citation-author" data-delta="11">B.K. Menon</span> and <span class="highwire-citation-author" data-delta="12">W. Qiu</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">June 2023,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(6) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">641-648; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7878 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-102697--61263056344" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">K. Zhu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</div><div class='author-affiliation'><span class='nlm-sup'>d</span>College of Electronic Engineering (K.Z.), Xi’an Shiyou University, Xi’an, Shaanxi, China</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=K.%2BZhu%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=Zhu%20K&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%3AK.%2BZhu%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/0000-0002-9363-7304" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K. Zhu</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">F. Bala </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</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=F.%2BBala%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=Bala%20F&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%3AF.%2BBala%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/0000-0001-6748-2081" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for F. Bala</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">J. Zhang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</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.%2BZhang%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=Zhang%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"><a href="/search/author1%3AJ.%2BZhang%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/0000-0002-0330-6908" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Zhang</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">F. Benali </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</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=F.%2BBenali%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=Benali%20F&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%3AF.%2BBenali%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/0000-0002-1043-1125" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for F. Benali</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">P. Cimflova </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Medicine, and Department of Radiology (P.C., M.D.H., A.D.), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada</div><div class='author-affiliation'><span class='nlm-sup'>e</span>St. Anne’s University Hospital Brno and Faculty of Medicine (P.C.), Masaryk University, Brno, Czech Republic</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=P.%2BCimflova%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=Cimflova%20P&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%3AP.%2BCimflova%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/0000-0001-8058-383X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for P. Cimflova</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">B.J. Kim </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>Department of Neurology and Cerebrovascular Center (B.J.K.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea</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.J.%2BKim%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=Kim%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%3AB.J.%2BKim%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/0000-0002-2719-3012" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B.J. Kim</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">R. McDonough </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</div><div class='author-affiliation'><span class='nlm-sup'>g</span>Department of Diagnostic and Interventional Neuroradiology (R.M.), University Hospital Hamburg, Hamburg, Germany</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=R.%2BMcDonough%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=McDonough%20R&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%3AR.%2BMcDonough%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/0000-0002-8060-4490" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R. McDonough</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">N. Singh </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</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=N.%2BSingh%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=Singh%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%3AN.%2BSingh%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/0000-0003-3647-0904" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for N. Singh</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">M.D. Hill </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Community Health Sciences (M.D.H.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Medicine, and Department of Radiology (P.C., M.D.H., A.D.), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada</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=M.D.%2BHill%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=Hill%20M&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%3AM.D.%2BHill%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/0000-0002-6269-1543" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.D. Hill</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">M. Goyal </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</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=M.%2BGoyal%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=Goyal%20M&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%3AM.%2BGoyal%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/0000-0001-9060-2109" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. Goyal</a></li></ul></div><div class="author-tooltip-10"><div class="author-tooltip-name">A. Demchuk </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Medicine, and Department of Radiology (P.C., M.D.H., A.D.), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada</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=A.%2BDemchuk%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=Demchuk%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%3AA.%2BDemchuk%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/0000-0002-4930-7789" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Demchuk</a></li></ul></div><div class="author-tooltip-11"><div class="author-tooltip-name">B.K. Menon </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)</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.K.%2BMenon%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=Menon%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%3AB.K.%2BMenon%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/0000-0002-3466-496X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B.K. Menon</a></li></ul></div><div class="author-tooltip-12"><div class="author-tooltip-name">W. Qiu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>h</span>School of Life Science and Technology (W.Q.), Huazhong University of Science and Technology, Wuhan, Hubei, China</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=W.%2BQiu%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=Qiu%20W&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%3AW.%2BQiu%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/0000-0001-7827-8270" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for W. Qiu</a></li></ul></div></div></div>  </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">K.</span> <span class="nlm-surname">Zhu</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Bala</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Zhang</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Benali</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Cimflova</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">B.J.</span> <span class="nlm-surname">Kim</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">R.</span> <span class="nlm-surname">McDonough</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">N.</span> <span class="nlm-surname">Singh</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">M.D.</span> <span class="nlm-surname">Hill</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Goyal</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Demchuk</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">B.K.</span> <span class="nlm-surname">Menon</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">W.</span> <span class="nlm-surname">Qiu</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">Jun 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(6) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">641-648; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7878 </span></div>
  
  
  
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      <div class="highwire-cite-title">Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model</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">K.</span> <span class="nlm-surname">Zhu</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Bala</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Zhang</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Benali</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Cimflova</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">B.J.</span> <span class="nlm-surname">Kim</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">R.</span> <span class="nlm-surname">McDonough</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">N.</span> <span class="nlm-surname">Singh</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">M.D.</span> <span class="nlm-surname">Hill</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Goyal</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Demchuk</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">B.K.</span> <span class="nlm-surname">Menon</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">W.</span> <span class="nlm-surname">Qiu</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">Jun 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(6) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">641-648; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7878 </span></div>
  
  
  
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