Linked Life Data

22003671

Source:http://linkedlifedata.com/resource/pubmed/id/22003671

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2011-10-18
pubmed:abstractText
Multi-angle total internal reflection fluorescence microscopy (MA-TIRFM) is a new generation of TIRF microscopy to study cellular processes near dorsal cell membrane in 4 dimensions (3D+t). To perform quantitative analysis using MA-TIRFM, it is necessary to track subcellular particles in these processes. In this paper, we propose a method based on a MAP framework for automatic particle tracking and apply it to track clathrin coated pits (CCPs). The expectation maximization (EM) algorithm is employed to solve the MAP problem. To provide the initial estimations for the EM algorithm, we develop a forward filter based on the most probable trajectory (MPT) filter. Multiple linear models are used to model particle dynamics. For CCP tracking, we use two linear models to describe constrained Brownian motion and fluorophore variation according to CCP properties. The tracking method is evaluated on synthetic data and results show that it has high accuracy. The result on real data confirmed by human expert cell biologists is also presented.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
629-36
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
An expectation maximization based method for subcellular particle tracking using multi-angle TIRF microscopy.
pubmed:affiliation
Yale University, New Haven, CT 06520, USA. liang.liang@yale.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural