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Marker Detection in Electron Tomography: A Comparative Study

Published online by Cambridge University Press:  25 November 2015

Patrick Trampert
Affiliation:
German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany Saarland University, 66123 Saarbrücken, Germany
Sviatoslav Bogachev
Affiliation:
Saarland University, 66123 Saarbrücken, Germany
Nico Marniok
Affiliation:
German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany Saarland University, 66123 Saarbrücken, Germany
Tim Dahmen*
Affiliation:
German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany Saarland University, 66123 Saarbrücken, Germany
Philipp Slusallek
Affiliation:
German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany Saarland University, 66123 Saarbrücken, Germany
*
* Corresponding author. Tim.Dahmen@dfki.de

Abstract

We conducted a comparative study of three widely used algorithms for the detection of fiducial markers in electron microscopy images. The algorithms were applied to four datasets from different sources. For the purpose of obtaining comparable results, we introduced figures of merit and implemented all three algorithms in a unified code base to exclude software-specific differences. The application of the algorithms revealed that none of the three algorithms is superior to the others in all cases. This leads to the conclusion that the choice of a marker detection algorithm highly depends on the properties of the dataset to be analyzed, even within the narrowed domain of electron tomography.

Type
Equipment and Techniques Development
Copyright
© Microscopy Society of America 2015 

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