Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2023 - 20242

Settings

COAR Access Rights: search.filters.coarAccessRights.metadata only accessSubject: search.filters.subject.Wound healing

Search Results

Now showing 1 - 2 of 2
  • Article
    Novel Neural Style Transfer Based Data Synthesis Method for Phase-Contrast Wound Healing Assay Images
    (Elsevier Ltd, 2024) Erdem,Y.S.; Iheme,L.O.; Uçar,M.; Özuysal,Ö.Y.; Balıkçı,M.; Morani,K.; Ünay,D.
    Recent advancements in the field of image synthesis have led to the development of Neural Style Transfer (NST) and Generative Adversarial Networks (GANs) which have proven to be powerful tools for data augmentation and realistic data generation. While GANs have been widely used for both data augmentation and generation, NST has not been employed for data generation tasks. Nonetheless, the simpler structure of NST compared to GANs makes it a promising alternative. In this research, we introduce an NST-based method for data generation, which to the best of our knowledge, is the first of its kind. By taking advantage of simplified architecture of NST models attributed to the utilization of a real image as the style input, our method enhances performance in data generation tasks under limited resource conditions. Additionally by utilizing patch-based training and high-resolution inference process high quality images are synthesized with limited resources. Furthermore multi-model and noised input is utilized for increased diversity with the novel NST-based data generation approach. Our proposed method utilizes binary segmentation maps as the condition input, representing the cell and wound regions. We evaluate the performance of our proposed NST-based method and compare it with a modified and fine-tuned conditional GAN (C-GAN) methods for the purpose of conditional generation of phase-contrast wound healing assay images. Through a series of quantitative and qualitative analyses, we demonstrate that our NST-based method outperforms the modified C-GAN while utilizing fewer resources. Additionally, we show that our NST-based method enhances segmentation performance when used as a data augmentation method. Our findings provide compelling evidence regarding the potential of NST for data generation tasks and its superiority over traditional GAN-based methods. The NST for data generation method was implemented in Python language and will be accessible at https://github.com/IDU-CVLab/NST_for_Gen under the MIT licence. © 2024 Elsevier Ltd
  • 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.