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Title: Cell segmentation of 2D phase-contrast microscopy images with deep learning method
Authors: Ayanzadeh, Aydın
Yağar, Hüseyin Onur
Yalçın Özuysal, Özden
Pesen Okvur, Devrim
Töreyin, Behçet Uğur
Unay, Devrim
Önal, Sevgi
Keywords: Deep learning
Phase-Contrast microscopy
Cell segmentation
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: 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.
Description: Medical Technologies Congress (TIPTEKNO) -- OCT 03-05, 2019 -- Izmir, TURKEY
ISBN: 978-1-7281-2420-9
Appears in Collections:Bioengineering / Biyomühendislik
Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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