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PLoS One. 2008 Apr 23;3(4):e1997. doi: 10.1371/journal.pone.0001997.

Automated three-dimensional detection and shape classification of dendritic spines from fluorescence microscopy images.

Author information

1
Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, United States of America.

Abstract

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function.

PMID:
18431482
PMCID:
PMC2292261
DOI:
10.1371/journal.pone.0001997
[Indexed for MEDLINE]
Free PMC Article

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