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.
- Bajaj, C., Bettadapura, R., Lei, N., Mollere, A., Peng, C., et al. (2010). Constructing A-spline weight functions for stable WEB-spline finite element methods. In Proceedings of the 14th ACM symposium on solid and physical modeling (pp. 153–158). ACM.Google Scholar
- Bajaj, C., Coyle, E., Lin, K. (1999). Tetrahedral meshes from planar cross sections. In Computer methods in applied mechanics and engineering (pp. 31–52).Google Scholar
- Bajaj, C., Pascucci, V., Schikore, D. (1996). Fast isocontouring for improved interactivity. In Proceedings of the 1996 symposium on volume visualization (pp. 39–46). IEEE.Google Scholar
- Bajaj, C., Pascucci, V., Schikore, D. (1997). The contour spectrum. In Proceedings of the 8th conference on visualization’97 (pp. 167–173). IEEE Computer Society Press.Google Scholar
- Boissonnat, J., & Geiger, B. (1992). Three-dimensional reconstruction of complex shapes based on the Delaunay triangulation. In Proceedings of SPIE (Vol. 964, pp. 964–975).Google Scholar
- Carr, H., Snoeyink, J., Axen, U. (2000). Computing contour trees in all dimensions. In Proceedings of the eleventh annual ACM-SIAM symposium on discrete algorithms (pp. 918–926). Society for Industrial and Applied Mathematics.Google Scholar
- CGAL (2013). Computational geometry algorithms library. http://www.cgal.org.
- CVC (2013). LBIE: level set boundary interior and exterior mesher. http://cvcweb.ices.utexas.edu/cvcwp/?page_id=103.
- De Rubeis, S., Fernández, E., Buzzi, A., Di Marino, D., Bagni, C. (2012). Molecular and cellular aspects of mental retardation in the fragile x syndrome: from gene mutation/s to spine dysmorphogenesis. In Synaptic plasticity (pp. 517–551).Google Scholar
- Garland, M. (2004). Qslim. http://mgarland.org/software/qslim.html.
- Jurrus, E., Watanabe, S., Giuly, R.J., Paiva, A.R., Ellisman, M.H., et al. (2012). Semi-automated neuron boundary detection and nonbranching process segmentation in electron microscopy images. Neuroinformatics, 11(1), 1–25.Google Scholar
- Kinney, J. (2009). Investigation of neurotransmitter diffusion in three-dimensional reconstructions of hippocampal neuropil. Ph.D. thesis, University of California, San Diego.Google Scholar
- O’Rourke, J. (1994). Computational geometry in C. Cambridge: Cambridge University.Google Scholar
- Shewchuk, J. (2002). What is a good linear finite element? Interpolation, conditioning, anisotropy, and quality measures (preprint). University of California at Berkeley.Google Scholar
- Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A. (2011). ilastik: interactive learning and segmentation toolkit. In 8th IEEE international symposium on biomedical imaging (ISBI 2011) (pp. 230–233).Google Scholar
- Stiles, J.R., Bartol, T.M., et al. (2001). Monte carlo methods for simulating realistic synaptic microphysiology using mcell. In: Computational neuroscience: realistic modeling for experimentalists, (pp. 87–128). Boca Raton: CRC.Google Scholar
- Turk, G., & O’Brien, J. (1999). Shape transformation using variational implicit functions. In SIGGRAPH’99 (pp. 335–342).Google Scholar
- Zhang, X., Bajaj, C.L., Kwon, B., Dolinsky, T.J., Nielsen, J.E., et al. (2006). Application of new multiresolution methods for the comparison of biomolecular electrostatic properties in the absence of global structural similarity. Multiscale Modeling & Simulation, 5, 1196–1213.CrossRefGoogle Scholar