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<meta name="DC.Title" content="Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF)" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8607" />
<meta name="DC.Date" content="2024-11-22" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset.

MATERIALS AND METHODS: In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.

RESULTS: Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.

CONCLUSIONS: The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.

ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95th Percentile Hausdorff Distance." />
<meta name="DC.Contributor" content="Elisa Colombo" />
<meta name="DC.Contributor" content="Mathijs de Boer" />
<meta name="DC.Contributor" content="Lambertus W. Bartels" />
<meta name="DC.Contributor" content="Luca P. Regli" />
<meta name="DC.Contributor" content="Tristan P.C. van Doormaal" />
<meta name="article:published_time" content="2024-11-22" />
<meta name="article:section" content="ORIGINAL RESEARCH" />
<meta name="citation_title" content="Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF)" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;ABSTRACT&lt;/h3&gt;
&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.&lt;/p&gt;&lt;p&gt;ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95&lt;sup&gt;th&lt;/sup&gt; Percentile Hausdorff Distance.&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="2024/11/22" />
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<meta name="citation_section" content="ORIGINAL RESEARCH" />
<meta name="citation_author" content="Elisa Colombo" />
<meta name="citation_author_institution" content="From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB)." />
<meta name="citation_author" content="Mathijs de Boer" />
<meta name="citation_author_institution" content="From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB)." />
<meta name="citation_author" content="Lambertus W. Bartels" />
<meta name="citation_author_institution" content="From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB)." />
<meta name="citation_author" content="Luca P. Regli" />
<meta name="citation_author_institution" content="From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB)." />
<meta name="citation_author" content="Tristan P.C. van Doormaal" />
<meta name="citation_author_institution" content="From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB)." />
<meta name="twitter:title" content="Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF)" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset.

MATERIALS AND METHODS: In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.

RESULTS: Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.

CONCLUSIONS: The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.

ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95th Percentile Hausdorff Distance." />
<meta name="og-title" property="og:title" content="Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF)" />
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<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: To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset.

MATERIALS AND METHODS: In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.

RESULTS: Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.

CONCLUSIONS: The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.

ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95th Percentile Hausdorff Distance." />
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      <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">ORIGINAL RESEARCH</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF)</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Elisa Colombo</span>, <span class="highwire-citation-author" data-delta="1">Mathijs de Boer</span>, <span class="highwire-citation-author" data-delta="2">Lambertus W. Bartels</span>, <span class="highwire-citation-author" data-delta="3">Luca P. Regli</span> and <span class="highwire-citation-author" data-delta="4">Tristan P.C. van Doormaal</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">November 2024,  </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">ajnr.A8607; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8607 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-111992--6903351217" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Elisa Colombo </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB).</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=Elisa%2BColombo%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=Colombo%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%3AElisa%2BColombo%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">Mathijs de Boer </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB).</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=Mathijs%2Bde%2BBoer%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=de%20Boer%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 last"><a href="/search/author1%3AMathijs%2Bde%2BBoer%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">Lambertus W. Bartels </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB).</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=Lambertus%2BW.%2BBartels%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=Bartels%20LW&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%3ALambertus%2BW.%2BBartels%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">Luca P. Regli </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB).</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=Luca%2BP.%2BRegli%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=Regli%20LP&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%3ALuca%2BP.%2BRegli%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">Tristan P.C. van Doormaal </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB).</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=Tristan%2BP.C.%2Bvan%2BDoormaal%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=van%20Doormaal%20TP&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%3ATristan%2BP.C.%2Bvan%2BDoormaal%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">ABSTRACT</h2><div id="sec-1" class="subsection"><p id="p-2"><strong>BACKGROUND AND PURPOSE:</strong> To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset.</p></div><div id="sec-2" class="subsection"><p id="p-3"><strong>MATERIALS AND METHODS:</strong> In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.</p></div><div id="sec-3" class="subsection"><p id="p-4"><strong>RESULTS:</strong> Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.</p></div><div id="sec-4" class="subsection"><p id="p-5"><strong>CONCLUSIONS:</strong> The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.</p><p id="p-6">ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95<sup>th</sup> Percentile Hausdorff Distance.</p></div></div><div class="section fn-group" id="fn-group-1"><h2>Footnotes</h2><ul><li class="fn-equal" id="fn-1"><p id="p-1"><a class="rev-xref" href="#xref-fn-1-1">↵</a><span class="fn-label">*</span> These authors contributed equally</p></li></ul></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2024 by American Journal of Neuroradiology</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div>  </div>

  
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    	<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">Nov 2024, </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">ajnr.A8607; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8607 </span></div>
  
  
  
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