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dc.contributor.authorHan, Renmin
dc.contributor.authorZhang, Fa
dc.contributor.authorGao, Xin
dc.date.accessioned2017-10-30T07:55:28Z
dc.date.available2017-10-30T07:55:28Z
dc.date.issued2017-10-20
dc.identifier.citationHan R, Zhang F, Gao X (2017) A fast fiducial marker tracking model for fully automatic alignment in electron tomography. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx653.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.pmid29069299
dc.identifier.doi10.1093/bioinformatics/btx653
dc.identifier.urihttp://hdl.handle.net/10754/625957
dc.description.abstractAutomatic 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
dc.description.sponsorshipWe 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).
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx653/4562325/A-fast-fiducial-marker-tracking-model-for-fully
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleA fast fiducial marker tracking model for fully automatic alignment in electron tomography
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionHigh Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
kaust.personHan, Renmin
kaust.personGao, Xin
kaust.grant.numberURF/1/1976-04
kaust.grant.numberURF/1/2602-01
kaust.grant.numberURF/1/3007-01
refterms.dateFOA2018-06-14T05:29:54Z


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com