Browsing by Author "Onal, Sevgi"

Now showing 1 - 2 of 2

Citation - WoS: 1
Citation - Scopus: 6
Automated Segmentation of Cells in Phase Contrast Optical Microscopy Time Series Images
(IEEE, 2019) Binici, Rifki Can; Sahin, Umut; Ayanzadeh, Aydin; Toreyin, Behcet Ugur; Onal, Sevgi; Okvur, Devrim Pesen; Ozuysal, Ozden Yalcin
Phase contrast optical microscopy is a preferred imaging technique for live-cell, temporal analysis. Segmentation of cells from time series data acquired with this technique is a labor-intensive and time-consuming task that cell biology researchers need solution for. In this study traditional image processing and deep learning based approaches for automated cell segmentation from phase contrast optical microscopy time series are presented, and their performances are evaluated against manually annotated datasets.
Citation - WoS: 7
Citation - Scopus: 11
Cell Segmentation of 2d Phase-Contrast Microscopy Images With Deep Learning Method
(IEEE, 2019) Ayanzadeh, Aydin; Yagar, Huseyin Onur; Ozuysal, Ozden Yalcin; Okvur, Devrim Pesen; Toreyin, Behcet Ugur; Unay, Devrim; Onal, Sevgi
The 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.