Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application To Wound Healing and Cell Motility Assays of Breast Cancer

dc.contributor.author Erdem, Yusuf Sait
dc.contributor.author Ayanzadeh, Aydın
dc.contributor.author Mayalı, Berkay
dc.contributor.author Balıkçı, Muhammed
dc.contributor.author Belli, Özge Nur
dc.contributor.author Uçar, Mahmut
dc.contributor.author Yalçın Özuysal, Özden
dc.contributor.author Pesen Okvur, Devrim
dc.contributor.author Önal, Sevgi
dc.contributor.author Morani, Kenan
dc.contributor.author Iheme, Leonardo Obinna
dc.contributor.author Töreyin, Behçet Uğur
dc.date.accessioned 2023-07-27T19:51:14Z
dc.date.available 2023-07-27T19:51:14Z
dc.date.issued 2023
dc.description.abstract This 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.identifier.doi 10.1016/B978-0-323-96129-5.00013-5
dc.identifier.isbn 9780323961295
dc.identifier.isbn 9780323996815
dc.identifier.scopus 2-s2.0-85161175350
dc.identifier.uri https://doi.org/10.1016/B978-0-323-96129-5.00013-5
dc.identifier.uri https://hdl.handle.net/11147/13668
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Breast cancer en_US
dc.subject Cell motility en_US
dc.subject Convolutional neural networks en_US
dc.subject Image processing en_US
dc.subject Quantification en_US
dc.subject Wound healing en_US
dc.title Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application To Wound Healing and Cell Motility Assays of Breast Cancer en_US
dc.type Book Part en_US
dspace.entity.type Publication
gdc.author.institutional Balıkçı, Muhammed
gdc.author.institutional Belli, Özge Nur
gdc.author.institutional Yalçın Özuysal, Özden
gdc.author.institutional Pesen Okvur, Devrim
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gdc.description.department İzmir Institute of Technology. Molecular Biology and Genetics en_US
gdc.description.endpage 154 en_US
gdc.description.publicationcategory Kitap Bölümü - Uluslararası en_US
gdc.description.scopusquality N/A
gdc.description.startpage 137 en_US
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