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

doi:10.1093/bioinformatics/btx653

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	A fast fiducial marker tracking model for fully automatic alignment in electron tomography
	Han, Renmin 1; Zhang, Fa 2; Gao, Xin 1
刊名	BIOINFORMATICS
	2018-03-01
卷号	34 期号:5 页码:853-863
ISSN号	1367-4803
DOI	10.1093/bioinformatics/btx653
英文摘要	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 version or later (also integrated in the AuTom platform at ext-link-http://ear.ict.ac.cn/) offers a complete implementation for fast alignment, in which fast fiducial marker tracking is available by the '-t' option.
资助项目	Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[URF/1/1976-04] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[URF/1/2602-01] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[URF/1/3007-01] ; NSFC[U1611263] ; NSFC[61232001] ; NSFC[61502455] ; NSFC[61472397] ; NSFC[U1611261] ; NSFC[61672493] ; National Key Research and Development Program of China[2017YFA0504702]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:000426813500018
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/6011]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gao, Xin
作者单位	1.KAUST, CBRC, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia 2.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Han, Renmin,Zhang, Fa,Gao, Xin. A fast fiducial marker tracking model for fully automatic alignment in electron tomography[J]. BIOINFORMATICS,2018,34(5):853-863.
APA	Han, Renmin,Zhang, Fa,&Gao, Xin.(2018).A fast fiducial marker tracking model for fully automatic alignment in electron tomography.BIOINFORMATICS,34(5),853-863.
MLA	Han, Renmin,et al."A fast fiducial marker tracking model for fully automatic alignment in electron tomography".BIOINFORMATICS 34.5(2018):853-863.