Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

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

Browse

Filters

2019 - 201922020 - 20226

Settings

Has files: YesPublisher: search.filters.publisher.IEEE

Search Results

Now showing 1 - 8 of 8
  • 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, Devrim
    We 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.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    Deep Learning Based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images
    (IEEE, 2020) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Okvur, Devrim Pesen; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, Devrim
    The segmentation of cells is necessary for biologists in the morphological statistics for quantitative and qualitative analysis in Phase-contrast Microscopy (PCM) images. In this paper, we address the cell segmentation problem in PCM images. Deep Neural Networks (DNNs) commonly is initialized with weights from a network pre-trained on a large annotated data set like ImageNet have superior performance than those trained from scratch on a small dataset. Here, we demonstrate how encoder-decoder type architectures such as U-Net and Feature Pyramid Network (FPN) can be improved by an alternative encoder which pre-trained on the ImageNet dataset. In particular, our experimental results confirm that the image descriptors from ResNet-18 are highly effective in accurate prediction of the cell boundary and have higher Intersection over Union (IoU) in comparison to the classical U-Net and require fewer training epochs.
  • 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.
  • Conference Object
    Citation - Scopus: 1
    Magnetic Levitation-Based Adipose Tissue Engineering Using Horizontal Magnet Deployment
    (IEEE, 2020) Sarıgil, Öykü; Tekin, Hüseyin Cumhur; Anıl İnevi, Müge; Anıl İnevi, Müge; Yılmaz, Esra; Sarıgil, Öykü; Özçelik, Özge; Meşe Özçivici, Gülistan; Meşe, Gülistan; Meşe Özçivici, Gülistan; Tekin, H. Cumhur
    Magnetic levitation is a promising technique for tissue engineering with contact- and label-free approach. Levitation-based biofabrication systems emerge as a simple, rapid and versatile alternative to traditional tissue culture systems, since biofabrication specs can easily be tailored via magnet shape and configuration. This study aims at possible magnetic levitation systems for culture of adipose tissue cells. In this study, we performed two different magnet configurations, vertical and horizontal deployment, in an effort to be utilized in adipose tissue engineering.
  • Conference Object
    Assessment of Cell Cycle and Viability of Magnetic Levitation Assembled Cellular Structures
    (IEEE, 2020) Anıl İnevi, Müge; Ünal, Yağmur Ceren; Yaman, Sena; Tekin, H. Cumhur; Meşe, Gülistan; Meşe, Gülistan
    Label-free magnetic levitation is one of the most recent Earth-based in vitro techniques that simulate the microgravity. This technique offers a great opportunity to biofabricate scaffold-free 3-dimensional (3D) structures and to study the effects of microgravity on these structures. In this study, self-assembled 3D living structures were fabricated in a paramagnetic medium by magnetic levitation technique and effects of the technique on cellular health was assessed. This magnetic force-assisted assembly system applied here offers broad applications in several fields, such as space biotechnology and bottom-up tissue engineering.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    Biofabrication of Cellular Structures Using Weightlessness as a Biotechnological Tool
    (IEEE, 2019) Anıl İnevi, Müge; Sarıgil, Öykü; Yaman, Sena; Yalçın Özuysal, Özden; Meşe, Gülistan; Tekin, Hüseyin Cumhur; Özçivici, Engin
    Gravity is an important biomechanical signal effecting the morphology and function of organisms. Reduction of gravitational forces, as experienced during spaceflight, cause alterations in the biological systems. Magnetic levitation technique is one of the most recent ground-based technology to mimic weightlessness environment. In addition to providing a platform to investigate biological effects of the weightlessness, this platform presents a novel opportunity to biofabricate 3-dimensional (3D) structures in a scaffold-and nozzle-free fashion. In this study, various controllable self-assembled 3D living structures were fabricated via magnetic levitation technique. This strategy may offer an easy and cost-effective opportunity for a wide range of space biotechnology researches.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 5
    Application of Magnetic Levitation Induced Weightlessness To Detect Cell Lineage
    (IEEE, 2019) Sarıgil, Öykü; Anıl İnevi, Müge; Yılmaz, Esra; Çağan, Melike; Meşe, Gülistan; Tekin, Hüseyin Cumhur; Özçivici, Engin
    Identification and classification of bone marrow cells is an important step for molecular biology and therapeutic studies related to bone marrow disorders such as osteoporosis or obesity. In this study, we applied magnetic levitation technology to induce a weightlessness environment to detect adipocytes and osteoblasts based on their single cell density. This biotechnological method can be used for separation of heterogeneous populations such as bone marrow once adapted to a continuous microfluidic platform.