Classification and uncertainty visualization of dendritic spines from optical microscopy imaging

Firdaus Janoos, Boonthanome Nouansengsy, Xiaoyin Xu, Raghu MacHiraju, Stephen T. Wong

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3-D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1-D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.

Original languageEnglish (US)
Pages (from-to)879-886
Number of pages8
JournalComputer Graphics Forum
Volume27
Issue number3
DOIs
StatePublished - Jan 1 2008

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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