Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T06:10:06.807Z Has data issue: false hasContentIssue false
  • English
  • Français

Marker Detection in Electron Tomography: A Comparative Study

Published online by Cambridge University Press:  25 November 2015

Patrick Trampert ,
Sviatoslav Bogachev ,
Nico Marniok ,
Tim Dahmen  and
Philipp Slusallek
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.[email protected]
Get access

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.

Keywords

marker detectionparticle detectionelectron tomographytomographic reconstruction
Type
Equipment and Techniques Development
Information
Microscopy and Microanalysis , Volume 21 , Issue 6 , December 2015 , pp. 1591 - 1601
Copyright
© Microscopy Society of America 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amat, F., Moussavi, F., Comolli, L.R., Elidan, G., Downing, K.H. & Horowitz, M. (2008). Markov random field based automatic image alignment for electron tomography. J Struct Biol 161, 260–275.CrossRefGoogle ScholarPubMed
Brandt, S., Heikkonen, J. & Engelhardt, P. (2001). Multiphase method for automatic alignment of transmission electron microscope images using markers. J Struct Biol 133, 10–22.Google Scholar
Cao, M., Takaoka, A., Zhang, H.-B. & Nishi, R. (2011). An automatic method of detecting and tracking fiducial markers for alignment in electron tomography. J Electron Microsc 60, 39–46.CrossRefGoogle ScholarPubMed
Cheng, C.-C., Chien, C.-C., Chen, H.-H., Hwu, Y. & Ching, Y.-T. (2014). Image alignment for tomography reconstruction from synchrotron X-ray microscopic images. PLoS One 9, e84675.Google Scholar
Dahmen, T., Marsallek, L., Turonova, B., Marniok, N., Bogachev, S., Nickels, S. & Slusallek, P. (2015). The Ettention software package. Ultramicroscopy (in press).Google Scholar
Diebolder, C.A., Beurskens, F.J., de Jong, R.N., Koning, R.I., Strumane, K., Lindorfer, M.A., Voorhorst, M., Ugurlar, D., Rosati, S., Heck, A.J.R., van de Winkel, J.G.J., Wilson, I.A., Koster, A.J., Taylor, R.P., Saphire, E.O., Burton, D.R., Schuurman, J., Gros, P. & Parren, P.W.H.I. (2014). Complement is activated by IgG hexamers assembled at the cell surface. Science 343, 1260–1263.Google Scholar
Fischler, M.A. & Bolles, R.C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24, 381–395.Google Scholar
Frank, J. (1992). Alignment by cross-correlation. In Electron Tomography , Frank, J. (Ed.), pp. 205–213. Boston, MA, USA: Springer.Google Scholar
Frank, J. (1996). Two-dimensional averaging techniques. In Three-Dimensional Electron Microscopy of Macromolecular Assemblies: Visualization of Biological Molecules in Their Native State, Frank, J. (Ed.), pp. 131–144. Oxford University Press: New York.Google Scholar
Hayashida, M., Terauchi, S. & Fujimoto, T. (2010). Automatic coarse-alignment for TEM tilt series of rod-shaped specimens collected with a full angular range. Micron (Oxford, England : 1993) 41, 540–545.Google Scholar
Hell, S.W. (2007). Far-field optical nanoscopy. Science 316, 1153–1158.CrossRefGoogle ScholarPubMed
Liu, Y., Penczek, P.A., McEwen, B.F. & Frank, J. (1995). A marker-free alignment method for electron tomography. Ultramicroscopy 58, 393–402.Google Scholar
Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60, 91–110.Google Scholar
Mastronarde, D.N. (2005). Fiducial marker and hybrid alignment methods for single- and double-axis tomography. In Electron Tomography, Frank, J. (Ed.), pp 163–185). Boston, MA: Springer.Google Scholar
Meyer-Ilse, W., Hamamoto, D., Nair, A., Leliévre, S.A., Denbeaux, G., Johnson, L., Pearson, A.L., Yager, D., Legros, M.A. & Larabell, C.A. (2001). High resolution protein localization using soft X-ray microscopy. J Microsc 201, 395–403.Google Scholar
Peckys, D.B., Bandmann, V. & de Jonge, N. (2014). Correlative fluorescence and scanning transmission electron microscopy of quantum dot-labeled proteins on whole cells in liquid. Methods Cell Biol 124, 305–322.Google Scholar
Pierson, J., Fernández, J.J., Bos, E., Amini, S., Gnaegi, H., Vos, M., Bel, B., Adolfsen, F., Carrascosa, J.L. & Peters, P.J. (2010). Improving the technique of vitreous cryo-sectioning for cryo-electron tomography: Electrostatic charging for section attachment and implementation of an anti-contamination glove box. J Struct Biol 169, 219–225.CrossRefGoogle ScholarPubMed
Ress, D., Harlow, M.L., Schwarz, M., Marshall, R.M. & McMahan, U.J. (1999). Automatic acquisition of fiducial markers and alignment of images in tilt series for electron tomography. J Electron Microsc 48, 277–287.Google Scholar
Sorzano, C.O.S., Messaoudi, C., Eibauer, M., Bilbao-Castro, J.R., Hegerl, R., Nickell, S., Marco, S. & Carazo, J.M. (2009). Marker-free image registration of electron tomography tilt-series. BMC Bioinformatics 10, 124.Google Scholar
Sosinsky, G.E., Crum, J., Jones, Y.Z., Lanman, J., Smarr, B., Terada, M., Martone, M.E., Deerinck, T.J., Johnson, J.E. & Ellisman, M.H. (2008). The combination of chemical fixation procedures with high pressure freezing and freeze substitution preserves highly labile tissue ultrastructure for electron tomography applications. J Struct Biol 161, 359–371.CrossRefGoogle ScholarPubMed
Tversky, A. (1977). Features of Similarity. Psych Reviews 84, 327–352.CrossRefGoogle Scholar
Voortman, L.M., Stallinga, S., Schoenmakers, R.H.M., van Vliet, L.J. & Rieger, B. (2011). A fast algorithm for computing and correcting the CTF for tilted, thick specimens in TEM. Ultramicroscopy 111, 1029–1036.CrossRefGoogle ScholarPubMed