Imaging cell clusters and tissue using learning tomography

M. Hasani Shoreh, A. Goy, J. Lim, U. Kamilov, M. Unser, D. Psaltis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We present an experimental comparison of optical tomography techniques: Learning Tomography (LT), an iterative method based on beam propagation model and diffraction tomography (DT) based Born and Rytov approximation. Imaging experiments were performed on yeast cells clusters fixed in a transparent agarose gel. In particular, we compare the LT with a similar iterative but linear method based on Rytov approximation.

Original languageEnglish
Title of host publicationOptical Methods for Inspection, Characterization, and Imaging of Biomaterials III
EditorsPietro Ferraro, Simonetta Grilli, Monika Ritsch-Marte, Christoph K. Hitzenberger
PublisherSPIE
ISBN (Electronic)9781510611115
DOIs
StatePublished - 2017
EventOptical Methods for Inspection, Characterization, and Imaging of Biomaterials III 2017 - Munich, Germany
Duration: Jun 26 2017Jun 28 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10333
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceOptical Methods for Inspection, Characterization, and Imaging of Biomaterials III 2017
Country/TerritoryGermany
CityMunich
Period06/26/1706/28/17

Keywords

  • Biological Imaging
  • Digital Holography
  • Machine Learning
  • Optical Tomography
  • Optimization

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