VolRoverN: Enhancing Surface and Volumetric Reconstruction for Realistic Dynamical Simulation of Cellular and Subcellular Function
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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.
KeywordsElectron microscopy Serial sections 3-D reconstruction Neuropil Skeletonization Reduced model Electrophysiology
We thank the anonymous reviewers for their many constructive comments that greatly improved the manuscript. Jose Rivera, Josef Spacek, Deborah Watson, Michael Chirillo and John Mendenhall assisted in various phases of this work. This work of CB, JE, ED was funded in part by NIH contracts R01-EB00487, R01-GM074258, and a grant from the UT-Portugal colab project. Work by TJS and TMB, was supported by NSF grant EMI9822, NIH grants MH079076 and P41-GM103712, that of DJ by NIH grants, MH048432 and MH094838 and of KH by NIH grants NS074644, NS21184, and the Texas Emerging Technologies Fund.
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