Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Karayegen, GokayHas files: No

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    Aligned Polyvinylpyrrolidone Nanofibers with Advanced Electrospinning for Biomedical Applications
    (2018) Karayegen, Gokay; Kocum, I. Cengiz; Serdaroglu, Dilek Cokeliler; Dogan, Mustafa; 0000-0001-5215-8887; 30400080; I-4296-2019
    BACKGROUND: Electrospinning is a highly effective method in order to generate nano-scaled fibers. In conventional electrospinning technique, geometry of nanofibers are mostly random due to the chaotic behavior of polymer jet. OBJECTIVE: Purpose of this study is to produce aligned nanofibers from PVP polymers with advanced electrospinning technique in order to be used in a potential novel sensor applications, tissue regeneration and engineering. METHODS: In this study, by using finite hollow cylinder focusing electrodes, an external electrostatic field is created. With these electrodes, it is aimed to decrease whipping instability of polymer jet. In addition, it is also investigated that the alignment ratio of nanofibers by using conductive parallel electrodes which placed through jet trajectory. RESULTS: In conclusion, with the effect of electrical field created by cylinder electrodes, radius of the fiber dispersion on the collector was able to be reduced and aligned nanofibers were successfully produced by using electrical field generated from the parallel plates. CONCLUSIONS: Radius of the fiber dispersion on the collector is 9.95 mm and fiber diameters varied between 800 nm and 3 mu m. Additionally, alignment ratio of the fibers is determined with ImageJ software. These alignment of nanofibers can be used in tissue engineering applications and sensor applications.
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    Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region
    (2021) Karayegen, Gokay; Aksahin, Mehmet Feyzi; 0000-0001-6294-9279
    When it comes to medical image segmentation on brain MR images, using deep learning techniques has a significant impact to predict tumor existence. Manual segmentation of a brain tumor is a time-consuming task and depends on knowledge and experience of physicians. In this paper, we present a semantic segmentation method by utilizing convolutional neural network to automatically segment brain tumor on 3D Brain Tumor Segmentation (BraTS) image data sets that comprise four different imaging modalities (T1, T1C, T2 and Flair). In addition, our study includes 3D imaging of whole brain and comparison between ground truth and predicted labels in 3D. In order to obtain exact tumor region and dimensions such as height, width and depth, this method was successfully applied and images were displayed different planes including sagittal, coronal and axial. Evaluation results of semantic segmentation which was executed by a deep learning network are significantly promising in terms of tumor prediction. Mean prediction ratio was determined as 91.718. Mean IoU (Intersection over Union) and Mean BF score were calculated as 86.946 and 92.938, respectively. Finally, dice scores of the test images were showed significant similarity between ground truth and predicted labels. As a result, both semantic segmentation metrics and 3D imaging can be interpreted as meaningful for diagnosing brain tumor accurately.