University of Oulu

Tomi Pitkäaho, Aki Manninen, and Thomas J. Naughton, "Focus prediction in digital holographic microscopy using deep convolutional neural networks," Appl. Opt. 58, A202-A208 (2019),

Focus prediction in digital holographic microscopy using deep convolutional neural networks

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Author: Pitkäaho, Tomi1; Manninen, Aki2; Naughton, Thomas J.1
Organizations: 1Department of Computer Science, Maynooth University–National University of Ireland Maynooth, Maynooth, County Kildare, Ireland
2Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, Oulu Center for Cell-Matrix Research, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.4 MB)
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Language: English
Published: Optical Society, 2019
Publish Date: 2021-03-15


Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its potential in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of Madin–Darby canine kidney cell clusters encoded in digital holograms. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, without performing numerical propagation. This paper reports on extensions to preliminary work published earlier as one of the first applications of deep learning in the field of digital holographic microscopy.

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Series: Applied optics
ISSN: 1559-128X
ISSN-E: 2155-3165
ISSN-L: 1559-128X
Volume: 58
Issue: 5
Pages: A202 - A208
DOI: 10.1364/AO.58.00A202
Type of Publication: A1 Journal article – refereed
Field of Science: 1182 Biochemistry, cell and molecular biology
Funding: This publication has emanated from research conducted with financial support from Science Foundation Ireland (SFI) under grant number 13/CDA/2224, and an Irish Research Council Postgraduate Scholarship.
Copyright information: © 2019 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.