Fast extraction of neuron morphologies from large-scale SBFSEM image stacks |
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Authors: | Stefan Lang Panos Drouvelis Enkelejda Tafaj Peter Bastian Bert Sakmann |
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Institution: | (1) Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany;(2) Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Martinsried, Germany;(3) Present address: Max Planck Florida Institute, 5353 Parkside Drive, MC19-RE, Jupiter, FL 33458-2906, USA |
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Abstract: | Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and
the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular
compartments, referred to as dendritic spines. The dimensions of a neuron’s dendritic compartment, including its spines, is
also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic
branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns,
changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure
is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique
make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed
for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms
for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual
neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic
branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures
and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite
for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines. |
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