Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 2Label-Free Detection of Rare Cancer Cells Using Deep Learning and Magnetic Levitation Principle(SPIE, 2021) Delikoyun, Kerem; Demir, Ali Aslan; Tekin, Hüseyin CumhurMagnetic levitation is an effective tool for separating target cells within a heterogeneous solution by utilizing density differences among cell lines. However, magnetic levitation cannot be used to identify target cells which have similar density profile as the other cells in the solution. Therefore, accuracy of cell identification can dramatically reduce. In this study, we introduce, for the first time, the use of deep learning-based object detection approach for label-free identification of rare cancer cells within levitated cells. As a result, our novel and hybrid detection strategy could be used to identify circulating tumor cells for diagnosis and prognosis of cancer. © 2021 SPIE.Conference Object Citation - WoS: 7Citation - Scopus: 11Cell Segmentation of 2d Phase-Contrast Microscopy Images With Deep Learning Method(Institute of Electrical and Electronics Engineers Inc., 2019) Ayanzadeh, Aydın; Yağar, Hüseyin Onur; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Unay, Devrim; Önal, SevgiThe quantitative and qualitative ascertainment of cell culture is integral to the robust determination of the cell structure analysis. Microscopy cell analysis and the epithet structures of cells in cell cultures are momentous in the fields of the biological research process. In this paper, we addressed the problem of phase-contrast microscopy under cell segmentation application. In our proposed method, we utilized the state-of-the-art deep learning models trained on our proposed dataset. Due to the low number of annotated images, we propose a multi-resolution network which is based on the U-Net architecture. Moreover, we applied multi-combination augmentation to our dataset which has increased the performance of segmentation accuracy significantly. Experimental results suggest that the proposed model provides superior performance in comparison to traditional state-of-the-art segmentation algorithms.