Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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Book Part Citation - Scopus: 1Automated 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 UğurThis 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 Detection and Restoration Pipeline for Phase Contrast Microscopy Time Series Images(IEEE, 2022) Iheme, Leonardo O.; Uçar, Mahmut; Önal, Sevgi; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet U.; Ünay, DevrimWe propose a pre-processing pipeline for the de-tection and restoration of distorted frames in phase-contrast microscopy time-series images. The analysis is based on the average intensity values of the frames within any given time- series image. The extent of the correction of intensity variation in frames is determined by the normalization of the difference between the current frame's average intensity and the median of average intensity of all frames. Our restoration algorithm preserves regional trans-passing pixels, does not cause new distortions, and increases the histogram similarity between the distorted and non-distorted frames. The algorithm was validated on 15,395 time-series image frames from 27 experiments and the results were found to be visually and quantitatively accurate.