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<meta name="DC.Title" content="Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8581" />
<meta name="DC.Date" content="2024-11-12" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent NIH data sharing mandates, novel solutions are a critical need.

MATERIALS AND METHODS: To develop an AI-powered tool for automatic defacing of pediatric brain MRIs, deep learning methodologies (nnU-Net) were employed using a large, diverse multi-institutional dataset of clinical radiology images. This included multi-parametric MRIs (T1w, T1w-contrast enhanced, T2w, T2w-FLAIR) with 976 total images from 208 brain tumor patients (Children’s Brain Tumor Network, CBTN) and 36 clinical control patients (Scans with Limited Imaging Pathology, SLIP) ranging in age from 7 days to 21 years old.

RESULTS: Face and ear removal accuracy for withheld testing data was the primary measure of model performance. Potential influences of defacing on downstream research usage were evaluated with standard image processing and AI-based pipelines. Group-level statistical trends were compared between original (non-defaced) and defaced images. Across image types, the model had high accuracy for removing face regions (mean accuracy, 98%; N =98 subjects/392 images), with lower performance for removal of ears (73%). Analysis of global and regional brain measures (SLIP cohort) showed minimal differences between original and defaced outputs (mean r S=0.93, all p &lt; 0.0001). AI-generated whole brain and tumor volumes (CBTN cohort) and temporalis muscle metrics (volume, cross-sectional area, centile scores; SLIP cohort) were not significantly affected by image defacing (all r S&gt;0.9, p &lt;0.0001).

CONCLUSIONS: The defacing model demonstrates efficacy in removing facial regions across multiple MRI types and exhibits minimal impact on downstream research usage. A software package with the trained model is freely provided for wider use and further development (pediatric-auto-defacer; &lt;https://github.com/d3b-center/pediatric-auto-defacer-public&gt;). By offering a solution tailored to pediatric cases and multiple MRI sequences, this defacing tool will expedite research efforts and promote broader adoption of data sharing practices within the neuroscience community.

ABBREVIATIONS: AI = artificial intelligence; CBTN = Children’s Brain Tumor Network; CSA = cross-sectional area; SLIP = Scans with Limited Imaging Pathology; TMT = temporalis muscle thickness; NIH = National Institute of Health; LH = left hemisphere; RH = right hemisphere." />
<meta name="DC.Contributor" content="Ariana M. Familiar" />
<meta name="DC.Contributor" content="Neda Khalili" />
<meta name="DC.Contributor" content="Nastaran Khalili" />
<meta name="DC.Contributor" content="Cassidy Schuman" />
<meta name="DC.Contributor" content="Evan Grove" />
<meta name="DC.Contributor" content="Karthik Viswanathan" />
<meta name="DC.Contributor" content="Jakob Seidlitz" />
<meta name="DC.Contributor" content="Aaron Alexander-Bloch" />
<meta name="DC.Contributor" content="Anna Zapaishchykova" />
<meta name="DC.Contributor" content="Benjamin H. Kann" />
<meta name="DC.Contributor" content="Arastoo Vossough" />
<meta name="DC.Contributor" content="Phillip B. Storm" />
<meta name="DC.Contributor" content="Adam C. Resnick" />
<meta name="DC.Contributor" content="Anahita Fathi Kazerooni" />
<meta name="DC.Contributor" content="Ali Nabavizadeh" />
<meta name="article:published_time" content="2024-11-13" />
<meta name="article:section" content="ORIGINAL RESEARCH" />
<meta name="citation_title" content="Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs" />
<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;Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent NIH data sharing mandates, novel solutions are a critical need.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;To develop an AI-powered tool for automatic defacing of pediatric brain MRIs, deep learning methodologies (nnU-Net) were employed using a large, diverse multi-institutional dataset of clinical radiology images. This included multi-parametric MRIs (T1w, T1w-contrast enhanced, T2w, T2w-FLAIR) with 976 total images from 208 brain tumor patients (Children’s Brain Tumor Network, CBTN) and 36 clinical control patients (Scans with Limited Imaging Pathology, SLIP) ranging in age from 7 days to 21 years old.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Face and ear removal accuracy for withheld testing data was the primary measure of model performance. Potential influences of defacing on downstream research usage were evaluated with standard image processing and AI-based pipelines. Group-level statistical trends were compared between original (non-defaced) and defaced images. Across image types, the model had high accuracy for removing face regions (mean accuracy, 98%; &lt;i&gt;N&lt;/i&gt;=98 subjects/392 images), with lower performance for removal of ears (73%). Analysis of global and regional brain measures (SLIP cohort) showed minimal differences between original and defaced outputs (mean &lt;i&gt;r&lt;/i&gt;&lt;sub&gt;S&lt;/sub&gt;=0.93, all &lt;i&gt;p&lt;/i&gt; &amp;lt; 0.0001). AI-generated whole brain and tumor volumes (CBTN cohort) and temporalis muscle metrics (volume, cross-sectional area, centile scores; SLIP cohort) were not significantly affected by image defacing (all &lt;i&gt;r&lt;/i&gt;&lt;sub&gt;S&lt;/sub&gt;&amp;gt;0.9, &lt;i&gt;p&lt;/i&gt;&amp;lt;0.0001).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The defacing model demonstrates efficacy in removing facial regions across multiple MRI types and exhibits minimal impact on downstream research usage. A software package with the trained model is freely provided for wider use and further development (pediatric-auto-defacer; https://github.com/d3b-center/pediatric-auto-defacer-public). By offering a solution tailored to pediatric cases and multiple MRI sequences, this defacing tool will expedite research efforts and promote broader adoption of data sharing practices within the neuroscience community.&lt;/p&gt;&lt;p&gt;ABBREVIATIONS: AI = artificial intelligence; CBTN = Children’s Brain Tumor Network; CSA = cross-sectional area; SLIP = Scans with Limited Imaging Pathology; TMT = temporalis muscle thickness; NIH = National Institute of Health; LH = left hemisphere; RH = right hemisphere.&lt;/p&gt;" />
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<meta name="citation_publication_date" content="2024/11/13" />
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<meta name="citation_author_institution" content="Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Lifespan Brain Institute at the Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="citation_author" content="Aaron Alexander-Bloch" />
<meta name="citation_author_institution" content="Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Lifespan Brain Institute at the Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="citation_author" content="Anna Zapaishchykova" />
<meta name="citation_author_institution" content="Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA" />
<meta name="citation_author_institution" content="Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA" />
<meta name="citation_author" content="Benjamin H. Kann" />
<meta name="citation_author_institution" content="Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA" />
<meta name="citation_author_institution" content="Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA" />
<meta name="citation_author" content="Arastoo Vossough" />
<meta name="citation_author_institution" content="Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Division of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="citation_author" content="Phillip B. Storm" />
<meta name="citation_author_institution" content="Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA." />
<meta name="citation_author" content="Adam C. Resnick" />
<meta name="citation_author_institution" content="Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA." />
<meta name="citation_author" content="Anahita Fathi Kazerooni" />
<meta name="citation_author_institution" content="Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA." />
<meta name="citation_author_institution" content="AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="citation_author" content="Ali Nabavizadeh" />
<meta name="citation_author_institution" content="Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA" />
<meta name="citation_author_institution" content="Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA" />
<meta name="twitter:title" content="Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs" />
<meta name="twitter:card" content="summary" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent NIH data sharing mandates, novel solutions are a critical need.

MATERIALS AND METHODS: To develop an AI-powered tool for automatic defacing of pediatric brain MRIs, deep learning methodologies (nnU-Net) were employed using a large, diverse multi-institutional dataset of clinical radiology images. This included multi-parametric MRIs (T1w, T1w-contrast enhanced, T2w, T2w-FLAIR) with 976 total images from 208 brain tumor patients (Children’s Brain Tumor Network, CBTN) and 36 clinical control patients (Scans with Limited Imaging Pathology, SLIP) ranging in age from 7 days to 21 years old.

RESULTS: Face and ear removal accuracy for withheld testing data was the primary measure of model performance. Potential influences of defacing on downstream research usage were evaluated with standard image processing and AI-based pipelines. Group-level statistical trends were compared between original (non-defaced) and defaced images. Across image types, the model had high accuracy for removing face regions (mean accuracy, 98%; N =98 subjects/392 images), with lower performance for removal of ears (73%). Analysis of global and regional brain measures (SLIP cohort) showed minimal differences between original and defaced outputs (mean r S=0.93, all p &lt; 0.0001). AI-generated whole brain and tumor volumes (CBTN cohort) and temporalis muscle metrics (volume, cross-sectional area, centile scores; SLIP cohort) were not significantly affected by image defacing (all r S&gt;0.9, p &lt;0.0001).

CONCLUSIONS: The defacing model demonstrates efficacy in removing facial regions across multiple MRI types and exhibits minimal impact on downstream research usage. A software package with the trained model is freely provided for wider use and further development (pediatric-auto-defacer; &lt;https://github.com/d3b-center/pediatric-auto-defacer-public&gt;). By offering a solution tailored to pediatric cases and multiple MRI sequences, this defacing tool will expedite research efforts and promote broader adoption of data sharing practices within the neuroscience community.

ABBREVIATIONS: AI = artificial intelligence; CBTN = Children’s Brain Tumor Network; CSA = cross-sectional area; SLIP = Scans with Limited Imaging Pathology; TMT = temporalis muscle thickness; NIH = National Institute of Health; LH = left hemisphere; RH = right hemisphere." />
<meta name="og-title" property="og:title" content="Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/early/2024/11/12/ajnr.A8581" />
<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: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent NIH data sharing mandates, novel solutions are a critical need.

MATERIALS AND METHODS: To develop an AI-powered tool for automatic defacing of pediatric brain MRIs, deep learning methodologies (nnU-Net) were employed using a large, diverse multi-institutional dataset of clinical radiology images. This included multi-parametric MRIs (T1w, T1w-contrast enhanced, T2w, T2w-FLAIR) with 976 total images from 208 brain tumor patients (Children’s Brain Tumor Network, CBTN) and 36 clinical control patients (Scans with Limited Imaging Pathology, SLIP) ranging in age from 7 days to 21 years old.

RESULTS: Face and ear removal accuracy for withheld testing data was the primary measure of model performance. Potential influences of defacing on downstream research usage were evaluated with standard image processing and AI-based pipelines. Group-level statistical trends were compared between original (non-defaced) and defaced images. Across image types, the model had high accuracy for removing face regions (mean accuracy, 98%; N =98 subjects/392 images), with lower performance for removal of ears (73%). Analysis of global and regional brain measures (SLIP cohort) showed minimal differences between original and defaced outputs (mean r S=0.93, all p &lt; 0.0001). AI-generated whole brain and tumor volumes (CBTN cohort) and temporalis muscle metrics (volume, cross-sectional area, centile scores; SLIP cohort) were not significantly affected by image defacing (all r S&gt;0.9, p &lt;0.0001).

CONCLUSIONS: The defacing model demonstrates efficacy in removing facial regions across multiple MRI types and exhibits minimal impact on downstream research usage. A software package with the trained model is freely provided for wider use and further development (pediatric-auto-defacer; &lt;https://github.com/d3b-center/pediatric-auto-defacer-public&gt;). By offering a solution tailored to pediatric cases and multiple MRI sequences, this defacing tool will expedite research efforts and promote broader adoption of data sharing practices within the neuroscience community.

ABBREVIATIONS: AI = artificial intelligence; CBTN = Children’s Brain Tumor Network; CSA = cross-sectional area; SLIP = Scans with Limited Imaging Pathology; TMT = temporalis muscle thickness; NIH = National Institute of Health; LH = left hemisphere; RH = right hemisphere." />
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Ariana M. Familiar</span>, <span class="highwire-citation-author" data-delta="1">Neda Khalili</span>, <span class="highwire-citation-author" data-delta="2">Nastaran Khalili</span>, <span class="highwire-citation-author" data-delta="3">Cassidy Schuman</span>, <span class="highwire-citation-author" data-delta="4">Evan Grove</span>, <span class="highwire-citation-author" data-delta="5">Karthik Viswanathan</span>, <span class="highwire-citation-author" data-delta="6">Jakob Seidlitz</span>, <span class="highwire-citation-author" data-delta="7">Aaron Alexander-Bloch</span>, <span class="highwire-citation-author" data-delta="8">Anna Zapaishchykova</span>, <span class="highwire-citation-author" data-delta="9">Benjamin H. Kann</span>, <span class="highwire-citation-author" data-delta="10">Arastoo Vossough</span>, <span class="highwire-citation-author" data-delta="11">Phillip B. Storm</span>, <span class="highwire-citation-author" data-delta="12">Adam C. Resnick</span>, <span class="highwire-citation-author" data-delta="13">Anahita Fathi Kazerooni</span> and <span class="highwire-citation-author" data-delta="14">Ali Nabavizadeh</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.A8581; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8581 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-111846--61221071293" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Ariana M. Familiar </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span> Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA</div><div class='author-affiliation'><span class='nlm-sup'>2</span> Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA.</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=Ariana%2BM.%2BFamiliar%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=Familiar%20AM&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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author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Khalili%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 last"><a href="/search/author1%3ANeda%2BKhalili%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">Nastaran Khalili </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span> Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA</div><div class='author-affiliation'><span class='nlm-sup'>2</span> Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA.</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=Nastaran%2BKhalili%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=Khalili%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 last"><a href="/search/author1%3ANastaran%2BKhalili%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">Cassidy Schuman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>3</span> School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA</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=Cassidy%2BSchuman%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=Schuman%20C&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ACassidy%2BSchuman%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">Evan Grove </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>3</span> School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA</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=Evan%2BGrove%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=Grove%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%3AEvan%2BGrove%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">Karthik Viswanathan 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