Marker Detection in Electron Tomography: A Comparative Study

Patrick Trampert; Sviatoslav Bogachev; Nico Marniok; Tim Dahmen; Philipp Slusallek

In: Microscopy and Microanalysis, Vol. 21 (Issue 6), Pages 1591-1601, Cambridge Journals, 11/2015.


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.


Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence