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VolRoverN: Enhancing Surface and Volumetric Reconstruction for Realistic Dynamical Simulation of Cellular and Subcellular Function

Overview of attention for article published in Neuroinformatics, October 2013
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Title
VolRoverN: Enhancing Surface and Volumetric Reconstruction for Realistic Dynamical Simulation of Cellular and Subcellular Function
Published in
Neuroinformatics, October 2013
DOI 10.1007/s12021-013-9205-2
Pubmed ID
Authors

John Edwards, Eric Daniel, Justin Kinney, Tom Bartol, Terrence Sejnowski, Daniel Johnston, Kristen Harris, Chandrajit Bajaj

Abstract

Establishing meaningful relationships between cellular structure and function requires accurate morphological reconstructions. In particular, there is an unmet need for high quality surface reconstructions to model subcellular and synaptic interactions among neurons and glia at nanometer resolution. We address this need with VolRoverN, a software package that produces accurate, efficient, and automated 3D surface reconstructions from stacked 2D contour tracings. While many techniques and tools have been developed in the past for 3D visualization of cellular structure, the reconstructions from VolRoverN meet specific quality criteria that are important for dynamical simulations. These criteria include manifoldness, water-tightness, lack of self- and object-object-intersections, and geometric accuracy. These enhanced surface reconstructions are readily extensible to any cell type and are used here on spiny dendrites with complex morphology and axons from mature rat hippocampal area CA1. Both spatially realistic surface reconstructions and reduced skeletonizations are produced and formatted by VolRoverN for easy input into analysis software packages for neurophysiological simulations at multiple spatial and temporal scales ranging from ion electro-diffusion to electrical cable models.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 6%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 34%
Student > Ph. D. Student 8 25%
Student > Master 3 9%
Professor > Associate Professor 2 6%
Student > Doctoral Student 1 3%
Other 2 6%
Unknown 5 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 25%
Neuroscience 5 16%
Computer Science 4 13%
Physics and Astronomy 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 6 19%
Unknown 4 13%