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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  <dc:creator>Ayanzadeh, Aydin</dc:creator>
  <dc:creator>Ozuysal, Ozden Yalcin</dc:creator>
  <dc:creator>Okvur, Devrim Pesen</dc:creator>
  <dc:creator>Onal, Sevgi</dc:creator>
  <dc:creator>Toreyin, Behcet Ugur</dc:creator>
  <dc:creator>Unay, Devrim</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/237150</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:237150</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 29 2855-2868</dc:source>
  <dc:title>Improved cell segmentation using deep learning in label-free optical microscopy images</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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