Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9906
Title: Deep convolutional neural networks for viability analysis directly from cell holograms captured using lensless holographic microscopy
Authors: Delikoyun, Kerem
Çine, Ersin
Anıl İnevi, Müge
Özçivici, Engin
Özuysal, Mustafa
Tekin, Hüseyin Cumhur
Keywords: Cell viability analysis
Deep convolutional neural network
Lensless holographic microscopy
Issue Date: 2019
Publisher: The Chemical and Biological Microsystems Society (CBMS)
Abstract: Cell viability analysis is one of the most widely used protocols in the fields of biomedical sciences. Traditional methods are prone to human error and require high-cost and bulky instrumentations. Lensless digital inline holographic microscopy (LDIHM) offers low-cost and high resolution imaging. However, recorded holograms should be digitally reconstructed to obtain real images, which requires intense computational work. We introduce a deep transfer learning-based cell viability classification method that directly processes the hologram without reconstruction. This new model is only trained once and viability of each cell can be predicted from its hologram. © 2019 CBMS-0001.
Description: Chemical and Biological Microsystems Society (CBMS)
URI: https://hdl.handle.net/11147/9906
ISBN: 978-173341900-0
Appears in Collections:Bioengineering / Biyomühendislik
Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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