University of Oulu

Tomi Pitkäaho, Aki Manninen, and Thomas J. Naughton "Focus classification in digital holographic microscopy using deep convolutional neural networks", Proc. SPIE 10414, Advances in Microscopic Imaging, 104140K (28 July 2017); doi: 10.1117/12.2286161; https://doi.org/10.1117/12.2286161

Focus classification 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
2Biocenter Oulu, Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019042613368
Language: English
Published: SPIE, 2017
Publish Date: 2019-04-26
Description:

Abstract

In digital holographic microscopy, one often obtains an in-focus image of the sample by applying a focus metric to a stack of numerical reconstructions. We present an alternative approach using a deep convolutional neural network.

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Series: Progress in biomedical optics and imaging
ISSN: 1605-7422
ISSN-E: 2410-9045
ISSN-L: 1605-7422
ISBN: 978-1-5106-1287-7
ISBN Print: 978-1-5106-1286-0
Issue: 10414
Article number: 104140K
DOI: 10.1117/12.2286161
OADOI: https://oadoi.org/10.1117/12.2286161
Host publication: Proceedings Volume 10414, Advances in Microscopic Imaging
Conference: European Conferences on Biomedical Optics
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
217 Medical engineering
Subjects:
Copyright information: © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).