title: Marker Detection in Electron Tomography: A Comparative Study.
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.
authors.affiliation: 1German Research Center for Artificial Intelligence GmbH (DFKI),66123 Saarbrücken,Germany.
authors.forename: Patrick
authors.initials: P
authors.lastname: Trampert
authors.affiliation: 2Saarland University,66123 Saarbrücken,Germany.
authors.forename: Sviatoslav
authors.initials: S
authors.lastname: Bogachev
authors.affiliation: 1German Research Center for Artificial Intelligence GmbH (DFKI),66123 Saarbrücken,Germany.
authors.forename: Nico
authors.initials: N
authors.lastname: Marniok
authors.affiliation: 1German Research Center for Artificial Intelligence GmbH (DFKI),66123 Saarbrücken,Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Dahmen
authors.affiliation: 1German Research Center for Artificial Intelligence GmbH (DFKI),66123 Saarbrücken,Germany.
authors.forename: Philipp
authors.initials: P
authors.lastname: Slusallek
keywords: electron tomography; marker detection; particle detection; tomographic reconstruction