A Global Spatial Similarity Optimization Scheme to Track Large Numbers of Dendritic Spines in Time-Lapse Confocal Microscopy

Qing Li, Zhigang Deng, Yong Zhang, Xiaobo Zhou, U. Valentin Nagerl, Stephen T. C. Wong
IEEE Trans. Med. Imaging. 2011-03-01; 30(3): 632-641
DOI: 10.1109/tmi.2010.2090354

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1. IEEE Trans Med Imaging. 2011 Mar;30(3):632-41. doi: 10.1109/TMI.2010.2090354.
Epub 2010 Nov 1.

A global spatial similarity optimization scheme to track large numbers of
dendritic spines in time-lapse confocal microscopy.

Li Q(1), Deng Z, Zhang Y, Zhou X, Nägerl UV, Wong ST.

Author information:
(1)Computer Science Department, University of Houston, Houston, TX 77004, USA.

Dendritic spines form postsynaptic contact sites in the central nervous system.
The rapid and spontaneous morphology changes of spines have been widely observed
by neurobiologists. Determining the relationship between dendritic spine
morphology change and its functional properties such as memory learning is a
fundamental yet challenging problem in neurobiology research. In this paper, we
propose a novel algorithm to track the morphology change of multiple spines
simultaneously in time-lapse neuronal images based on nonrigid registration and
integer programming. We also propose a robust scheme to link
disappearing-and-reappearing spines. Performance comparisons with other
state-of-the-art cell and spine tracking algorithms, and the ground truth show
that our approach is more accurate and robust, and it is capable of tracking a
large number of neuronal spines in time-lapse confocal microscopy images.

DOI: 10.1109/TMI.2010.2090354
PMID: 21047709 [Indexed for MEDLINE]

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