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), https://doi.org/10.1364/AO.58.00A202
Focus prediction in digital holographic microscopy using deep convolutional neural networks
|Author:||Pitkäaho, Tomi1; Manninen, Aki2; Naughton, Thomas J.1|
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
|Online Access:||PDF Full Text (PDF, 3.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202103157352
|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.
|Pages:||A202 - A208|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1182 Biochemistry, cell and molecular biology
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.
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