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dc.contributor.authorAyanzadeh, Aydin
dc.contributor.authorYagar, Huseyin Onur
dc.contributor.authorOzuysal, Ozden Yalcin
dc.contributor.authorOkvur, Devrim Pesen
dc.contributor.authorToreyin, Behcet Ugur
dc.contributor.authorUnay, Devrim
dc.contributor.authorOnal, Sevgi
dc.descriptionMedical Technologies Congress (TIPTEKNO) -- OCT 03-05, 2019 -- Izmir, TURKEYen_US
dc.descriptionWOS: 000516830900023en_US
dc.description.abstractThe 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.en_US
dc.subjectDeep learningen_US
dc.subjectphase-contrast microscopyen_US
dc.subjectcell segmentationen_US
dc.titleCell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Methoden_US
dc.relation.journal2019 Medical Technologies Congress (Tiptekno)en_US
dc.contributor.departmentIzmir Institute of Technologyen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US

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