Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

Permanent URI for this collectionhttps://hdl.handle.net/11147/9

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Publication Index: search.filters.publicationIndex.ScopusSubject: search.filters.subject.Image processing

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  • Book Part
    Citation - Scopus: 1
    Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application To Wound Healing and Cell Motility Assays of Breast Cancer
    (Elsevier, 2023) 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
    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.
  • Conference Object
    Citation - Scopus: 2
    Yara İyileşmesi Mikroskopi Görüntü Serilerinin Otomatik Analizi - Bir Ön-çalışma
    (IEEE, 2020) Mayalı, Berkay; Şaylığ, Orkun; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Ünay, Devrim
    Collective cell analysis from microscopy image series is important for wound healing research. Computer-based automation of such analyses may help in rapid acquisition of reliable and reproducible results. In this study phase -contrast optical microscopy image series of an in-vitro wound healing essay is manually delineated by two experts and its analysis is realized, traditional image processing and deep learning based approaches for automated segmentation of wound area are developed and their perlOrmance comparisons are carried out.
  • Conference Object
    Citation - Scopus: 1
    A Preliminary Study on Cell Motility Analysis From Phase-Contrast Microscopy Image Series
    (IEEE, 2020) Kayan, Emre; Kavuşan, Tarık; Önal, Sevgi; Pesen Okvur, Devrim; Yalçın Özuysal, Özden; Töreyin, Behçet Uğur; Ünay, Devrim
    Analyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.
  • Article
    Citation - WoS: 246
    Citation - Scopus: 245
    Step-By Quantitative Analysis of Focal Adhesions
    (Elsevier Ltd., 2014) Horzum, Utku; Özdil, Berrin; Pesen Okvur, Devrim
    Focal adhesions (FAs) are specialized adhesive structures which serve as cellular communication units between cells and the surrounding extracellular matrix. FAs are involved in signal transduction and actin cytoskeleton organization. FAs mediate cell adhesion, which is a critical phenomenon in cancer research. Since cells can form many and micrometer scale FAs, their quantitative analysis demands well-optimized image analysis approaches [1-3]. Here, we have optimized the analysis of FAs of MDA-MB-231 breast cancer cells. The optimization is based on proper processing of immunofluorescence images of vinculin, which is one of the markers of FAs. All image processing steps are carried out using the ImageJ software, which is freely available and in the public domain. The advantages of our method are:The analysis steps are simplified by combining different plugins of the ImageJ program.FAs are better detected with minimal false negatives due to optimized processing of fluorescent images.This approach can be applied to quantify a variety of fluorescent images comprising focal and/or localized signals within a high background such as FAs, one of the many complex signaling structures in a cell.