A fast fiducial marker tracking model for fully automatic alignment in electron tomography

Renmin Han, Fa Zhang, Xin Gao

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Automatic alignment, especially fiducial marker-based alignment, has become increasingly important due to the high demand of subtomogram averaging and the rapid development of large-field electron microscopy. Among the alignment steps, fiducial marker tracking is a crucial one that determines the quality of the final alignment. Yet, it is still a challenging problem to track the fiducial markers accurately and effectively in a fully automatic manner.In this paper, we propose a robust and efficient scheme for fiducial marker tracking. Firstly, we theoretically prove the upper bound of the transformation deviation of aligning the positions of fiducial markers on two micrographs by affine transformation. Secondly, we design an automatic algorithm based on the Gaussian mixture model to accelerate the procedure of fiducial marker tracking. Thirdly, we propose a divide-and-conquer strategy against lens distortions to ensure the reliability of our scheme. To our knowledge, this is the first attempt that theoretically relates the projection model with the tracking model. The real-world experimental results further support our theoretical bound and demonstrate the effectiveness of our algorithm. This work facilitates the fully automatic tracking for datasets with a massive number of fiducial markers.The C/C ++ source code that implements the fast fiducial marker tracking is available at https://github.com/icthrm/gmm-marker-tracking. Markerauto 1.6 version or later (also integrated in the AuTom platform at http://ear.ict.ac.cn/) offers a complete implementation for fast alignment, in which fast fiducial marker tracking is available by the
Original languageEnglish (US)
Pages (from-to)853-863
Number of pages11
Issue number5
StatePublished - Oct 23 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/1976-04, URF/1/2602-01, URF/1/3007-01
Acknowledgements: We thank Lun Li and Peng Yang for their help in method implementation and the online platform maintenance. We are also grateful to Yu Li and Sheng Wang for proofreading the manuscript and for thoughtful discussions. This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. URF/1/1976-04, URF/1/2602-01, and URF/1/3007-01, the National Key Research and Development Program of China(2017YFA0504702), the NSFC projects Grant No.U1611263, U1611261, 61232001, 61472397, 61502455, 61672493, and Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).


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