1 Ocak 2021 Dergi makalesi Açık Erişim

Ayanzadeh, Aydin; Ozuysal, Ozden Yalcin; Okvur, Devrim Pesen; Onal, Sevgi; Toreyin, Behcet Ugur; Unay, Devrim

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/237150</identifier>
  <creators>
    <creator>
      <creatorName>Ayanzadeh, Aydin</creatorName>
      <givenName>Aydin</givenName>
      <familyName>Ayanzadeh</familyName>
      <affiliation>Istanbul Tech Univ, Informat Inst, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ozuysal, Ozden Yalcin</creatorName>
      <givenName>Ozden Yalcin</givenName>
      <familyName>Ozuysal</familyName>
      <affiliation>Izmir Inst Technol, Dept Mol Biol &amp; Genet, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Okvur, Devrim Pesen</creatorName>
      <givenName>Devrim Pesen</givenName>
      <familyName>Okvur</familyName>
      <affiliation>Izmir Inst Technol, Dept Mol Biol &amp; Genet, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Onal, Sevgi</creatorName>
      <givenName>Sevgi</givenName>
      <familyName>Onal</familyName>
      <affiliation>Izmir Inst Technol, Biotechnol &amp; Bioengn Grad Program, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Toreyin, Behcet Ugur</creatorName>
      <givenName>Behcet Ugur</givenName>
      <familyName>Toreyin</familyName>
      <affiliation>Istanbul Tech Univ, Informat Inst, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Unay, Devrim</creatorName>
      <givenName>Devrim</givenName>
      <familyName>Unay</familyName>
      <affiliation>Izmir Democracy Univ, Dept Elect &amp; Elect Engn, Izmir, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopy Images</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/237150</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3906/elk-2105-244</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">The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the decoder. This alteration makes the model superconvergent yielding improved performance results on two challenging optical microscopy image series: a phase-contrast dataset of our own (MDA-MB-231) and a brightfield dataset from a well-known challenge (DSB2018). We utilized the U-Net with pretrained ResNet-18 as the encoder for the segmentation task. Hence, following the modifications, we redesign a novel skip-connection to reduce the semantic gap between the encoder and the decoder. The proposed skip-connection increases the accuracy of the model on both datasets. The proposed segmentation approach results in Jaccard Index values of 85.0% and 89.2% on the DSB2018 and MDA-MB-231 datasets, respectively. The results reveal that our method achieves competitive results compared to the state-of-the-art approaches and surpasses the performance of baseline approaches.</description>
  </descriptions>
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