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Title: Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer
Authors: Erdem, Yusuf Sait
Ayanzadeh, Aydın
Mayalı, Berkay
Balıkçı, Muhammed
Belli, Özge Nur
Uçar, Mahmut
Yalçın Özuysal, Özden
Pesen Okvur, Devrim
Önal, Sevgi
Morani, Kenan
Iheme, Leonardo Obinna
Töreyin, Behçet Uğur
Keywords: Breast cancer
Cell motility
Convolutional neural networks
Image processing
Wound healing
Publisher: Elsevier
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.
ISBN: 9780323961295
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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