Dergi makalesi Açık Erişim
Ayanzadeh, Aydin; Ozuysal, Ozden Yalcin; Okvur, Devrim Pesen; Onal, Sevgi; Toreyin, Behcet Ugur; Unay, Devrim
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Ozuysal, Ozden Yalcin</subfield> <subfield code="u">Izmir Inst Technol, Dept Mol Biol & Genet, Izmir, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Okvur, Devrim Pesen</subfield> <subfield code="u">Izmir Inst Technol, Dept Mol Biol & Genet, Izmir, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Onal, Sevgi</subfield> <subfield code="u">Izmir Inst Technol, Biotechnol & Bioengn Grad Program, Izmir, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Toreyin, Behcet Ugur</subfield> <subfield code="u">Istanbul Tech Univ, Informat Inst, Istanbul, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Unay, Devrim</subfield> <subfield code="u">Izmir Democracy Univ, Dept Elect & Elect Engn, Izmir, Turkey</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="c">2855-2868</subfield> <subfield code="p">TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES</subfield> <subfield code="v">29</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="a">Creative Commons Attribution</subfield> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.3906/elk-2105-244</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Improved cell segmentation using deep learning in label-free optical microscopy images</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Ayanzadeh, Aydin</subfield> <subfield code="u">Istanbul Tech Univ, Informat Inst, Istanbul, Turkey</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:237150</subfield> <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2021-01-01</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/237150/files/bib-f65cee29-3ab4-4039-aa25-77321626f448.txt</subfield> <subfield code="z">md5:2cd42689243f763b855cbd9610dc93c2</subfield> <subfield code="s">253</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <controlfield tag="005">20221007095026.0</controlfield> <controlfield tag="001">237150</controlfield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a">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.</subfield> </datafield> </record>
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