Ayanzadeh, AydınYağar, Hüseyin OnurYalçın Özuysal, ÖzdenPesen Okvur, DevrimTöreyin, Behçet UğurUnay, DevrimÖnal, Sevgi04.03. Department of Molecular Biology and Genetics04. Faculty of Science01. Izmir Institute of Technology2020-07-252020-07-252019978-1-7281-2420-9https://hdl.handle.net/11147/9393Medical Technologies Congress (TIPTEKNO) -- OCT 03-05, 2019 -- Izmir, TURKEYThe 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.eninfo:eu-repo/semantics/openAccessDeep learningPhase-Contrast microscopyCell segmentationConference Object2-s2.0-85075595764