Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11785
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dc.contributor.authorAyanzadeh, Aydın-
dc.contributor.authorYalçın Özuysal, Özden-
dc.contributor.authorOkvur, Devrim Pesen-
dc.contributor.authorÖnal, Sevgi-
dc.contributor.authorTöreyin, Behçet Uğur-
dc.contributor.authorÜnay, Devrim-
dc.date.accessioned2021-12-02T18:16:11Z-
dc.date.available2021-12-02T18:16:11Z-
dc.date.issued2020-
dc.identifier.isbn978-1-7281-7206-4-
dc.identifier.issn2165-0608-
dc.identifier.urihttps://hdl.handle.net/11147/11785-
dc.description28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK -- Istanbul Medipol Univen_US
dc.description.abstractThe segmentation of cells is necessary for biologists in the morphological statistics for quantitative and qualitative analysis in Phase-contrast Microscopy (PCM) images. In this paper, we address the cell segmentation problem in PCM images. Deep Neural Networks (DNNs) commonly is initialized with weights from a network pre-trained on a large annotated data set like ImageNet have superior performance than those trained from scratch on a small dataset. Here, we demonstrate how encoder-decoder type architectures such as U-Net and Feature Pyramid Network (FPN) can be improved by an alternative encoder which pre-trained on the ImageNet dataset. In particular, our experimental results confirm that the image descriptors from ResNet-18 are highly effective in accurate prediction of the cell boundary and have higher Intersection over Union (IoU) in comparison to the classical U-Net and require fewer training epochs.en_US
dc.description.sponsorshipThe data used in this study is collected under the Marie Curie IRG grant (no: FP7 PIRG08-GA-2010-27697). Aydin Ayanzadeh's work is supported, in part, by Vodafone Turkey, under project no. ITUVF20180901P04 within the context of ITU Vodafone Future Lab RD program. This work is in part funded by ITU BAP MGA-2017-40964. This work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 119E578.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2020 28Th Signal Processing and Communications Applications Conference (SIU)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCell segmentationen_US
dc.subjectPhase-Contrast microscopyen_US
dc.subjectDeep learningen_US
dc.titleDeep learning based segmentation pipeline for label-free phase-contrast microscopy imagesen_US
dc.typeConference Objecten_US
dc.authorid0000-0003-4406-2783-
dc.authorid0000-0002-8816-3204-
dc.authorid0000-0002-9882-132X-
dc.institutionauthorYalçın Özuysal, Özden-
dc.institutionauthorOkvur, Devrim Pesen-
dc.institutionauthorÖnal, Sevgi-
dc.departmentİzmir Institute of Technology. Molecular Biology and Geneticsen_US
dc.identifier.wosWOS:000653136100278en_US
dc.identifier.scopus2-s2.0-85100321434en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorwosidToreyin, Behcet Ugur/ABI-6849-2020-
dc.authorwosidOnal, Sevgi/AAO-8438-2021-
dc.authorwosidAyanzadeh, Aydin/O-8380-2019-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept04.03. Department of Molecular Biology and Genetics-
crisitem.author.dept04.03. Department of Molecular Biology and Genetics-
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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