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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 Quantification 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. | URI: | https://doi.org/10.1016/B978-0-323-96129-5.00013-5 https://hdl.handle.net/11147/13668 |
ISBN: | 9780323961295 9780323996815 |
Appears in Collections: | Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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