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

Handle URI:
http://hdl.handle.net/10754/625957
Title:
A fast fiducial marker tracking model for fully automatic alignment in electron tomography
Authors:
Han, Renmin; Zhang, Fa; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
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
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Han 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.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
URF/1/1976-04; URF/1/2602-01; URF/1/3007-01
Issue Date:
20-Oct-2017
DOI:
10.1093/bioinformatics/btx653
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
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).
Additional Links:
https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx653/4562325/A-fast-fiducial-marker-tracking-model-for-fully
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHan, Renminen
dc.contributor.authorZhang, Faen
dc.contributor.authorGao, Xinen
dc.date.accessioned2017-10-30T07:55:28Z-
dc.date.available2017-10-30T07:55:28Z-
dc.date.issued2017-10-20en
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.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.doi10.1093/bioinformatics/btx653en
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 theen
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).en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx653/4562325/A-fast-fiducial-marker-tracking-model-for-fullyen
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.comen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.titleA fast fiducial marker tracking model for fully automatic alignment in electron tomographyen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionHigh Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.en
kaust.authorHan, Renminen
kaust.authorGao, Xinen
kaust.grant.numberURF/1/1976-04en
kaust.grant.numberURF/1/2602-01en
kaust.grant.numberURF/1/3007-01en
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