1 Ocak 2021 Dergi makalesi Açık Erişim

Alis, Deniz; Yergin, Mert; Alis, Ceren; Topel, Cagdas; Asmakutlu, Ozan; Bagcilar, Omer; Senli, Yeseren Deniz; Ustundag, Ahmet; Salt, Vefa; Dogan, Sebahat Nacar; Velioglu, Murat; Selcuk, Hakan Hatem; Kara, Batuhan; Oksuz, Ilkay; Kizilkilic, Osman; Karaarslan, Ercan

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/235848</identifier>
  <creators>
    <creator>
      <creatorName>Alis, Deniz</creatorName>
      <givenName>Deniz</givenName>
      <familyName>Alis</familyName>
      <affiliation>Acibadem Mehmet Ali Aydinlar Univ, Dept Radiol, Sch Med, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yergin, Mert</creatorName>
      <givenName>Mert</givenName>
      <familyName>Yergin</familyName>
      <affiliation>Bahcesehir Univ, Dept Software Engn &amp; Appl Sci, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Alis, Ceren</creatorName>
      <givenName>Ceren</givenName>
      <familyName>Alis</familyName>
      <affiliation>Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Neurol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Topel, Cagdas</creatorName>
      <givenName>Cagdas</givenName>
      <familyName>Topel</familyName>
      <affiliation>Istanbul Mehmet Akif Ersoy Thorac &amp; Cardiovasc Su, Dept Radiol, Halkali Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Asmakutlu, Ozan</creatorName>
      <givenName>Ozan</givenName>
      <familyName>Asmakutlu</familyName>
      <affiliation>Istanbul Mehmet Akif Ersoy Thorac &amp; Cardiovasc Su, Dept Radiol, Halkali Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Bagcilar, Omer</creatorName>
      <givenName>Omer</givenName>
      <familyName>Bagcilar</familyName>
      <affiliation>Istanbul Silivri State Hosp, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Senli, Yeseren Deniz</creatorName>
      <givenName>Yeseren Deniz</givenName>
      <familyName>Senli</familyName>
      <affiliation>Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ustundag, Ahmet</creatorName>
      <givenName>Ahmet</givenName>
      <familyName>Ustundag</familyName>
      <affiliation>Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Salt, Vefa</creatorName>
      <givenName>Vefa</givenName>
      <familyName>Salt</familyName>
      <affiliation>Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Dogan, Sebahat Nacar</creatorName>
      <givenName>Sebahat Nacar</givenName>
      <familyName>Dogan</familyName>
      <affiliation>Istanbul Gaziosmanpasa Training &amp; Res Hosp, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Velioglu, Murat</creatorName>
      <givenName>Murat</givenName>
      <familyName>Velioglu</familyName>
      <affiliation>Istanbul Fatih Sultan Mehmet Training &amp; Res Hosp, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Selcuk, Hakan Hatem</creatorName>
      <givenName>Hakan Hatem</givenName>
      <familyName>Selcuk</familyName>
      <affiliation>Istanbul Bakirkoy Sadi Konuk Training &amp; Res Hosp, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kara, Batuhan</creatorName>
      <givenName>Batuhan</givenName>
      <familyName>Kara</familyName>
      <affiliation>Istanbul Bakirkoy Sadi Konuk Training &amp; Res Hosp, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Oksuz, Ilkay</creatorName>
      <givenName>Ilkay</givenName>
      <familyName>Oksuz</familyName>
      <affiliation>Istanbul Tech Univ, Dept Software Engn &amp; Appl Sci, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kizilkilic, Osman</creatorName>
      <givenName>Osman</givenName>
      <familyName>Kizilkilic</familyName>
      <affiliation>Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Karaarslan, Ercan</creatorName>
      <givenName>Ercan</givenName>
      <familyName>Karaarslan</familyName>
      <affiliation>Acibadem Mehmet Ali Aydinlar Univ, Dept Radiol, Sch Med, Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Inter-Vendor Performance Of Deep Learning In Segmenting Acute Ischemic Lesions On Diffusion-Weighted Imaging: A Multicenter Study</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/235848</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1038/s41598-021-91467-x</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n=2986) and B (n=3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.</description>
  </descriptions>
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