Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/13668
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dc.contributor.authorErdem, Yusuf Sait-
dc.contributor.authorAyanzadeh, Aydın-
dc.contributor.authorMayalı, Berkay-
dc.contributor.authorBalıkçı, Muhammed-
dc.contributor.authorBelli, Özge Nur-
dc.contributor.authorUçar, Mahmut-
dc.contributor.authorYalçın Özuysal, Özden-
dc.contributor.authorPesen Okvur, Devrim-
dc.contributor.authorÖnal, Sevgi-
dc.contributor.authorMorani, Kenan-
dc.contributor.authorIheme, Leonardo Obinna-
dc.contributor.authorTöreyin, Behçet Uğur-
dc.date.accessioned2023-07-27T19:51:14Z-
dc.date.available2023-07-27T19:51:14Z-
dc.date.issued2023-
dc.identifier.isbn9780323961295-
dc.identifier.isbn9780323996815-
dc.identifier.urihttps://doi.org/10.1016/B978-0-323-96129-5.00013-5-
dc.identifier.urihttps://hdl.handle.net/11147/13668-
dc.description.abstractThis chapter describes a workflow for analyzing phase-contrast microscopy (PCM) data from two fundamental types of biomedical assays: assays for cell motility and assays for wound healing. The workflow of the analysis is composed of the methods for acquiring, restoring, segmenting, and quantifying biomedical data. In the literature, there have been separate methods aimed at specific stages of PCM data analysis. Nonetheless, there has never been a complete workflow for all stages of analysis. This work is an innovation that proposes an end-to-end workflow for image pre-processing, deep learning segmentation, tracking, and quantification stages in cell motility and wound healing assay analyses. The findings indicate that domain knowledge can be used to make simple but significant improvements to the results of cutting-edge methods. Furthermore, even for deep learning-based methods, pre-processing is clearly a necessary step in the workflow. © 2023 Elsevier Inc. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofDiagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methodsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast canceren_US
dc.subjectCell motilityen_US
dc.subjectConvolutional neural networksen_US
dc.subjectImage processingen_US
dc.subjectQuantificationen_US
dc.subjectWound healingen_US
dc.titleAutomated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast canceren_US
dc.typeBook Parten_US
dc.institutionauthorBalıkçı, Muhammed-
dc.institutionauthorBelli, Özge Nur-
dc.institutionauthorYalçın Özuysal, Özden-
dc.institutionauthorPesen Okvur, Devrim-
dc.departmentİzmir Institute of Technology. Molecular Biology and Geneticsen_US
dc.identifier.startpage137en_US
dc.identifier.endpage154en_US
dc.identifier.scopus2-s2.0-85161175350en_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.identifier.doi10.1016/B978-0-323-96129-5.00013-5-
dc.authorscopusid57216734973-
dc.authorscopusid57189501659-
dc.authorscopusid58305418900-
dc.authorscopusid58305217200-
dc.authorscopusid58304201300-
dc.authorscopusid56448409800-
dc.authorscopusid58304809100-
item.grantfulltextnone-
item.openairetypeBook Part-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
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
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