Two-Phased Real-Time Rendering of Large Neuron Databases

Ciechomski, Pablo de Heras; Mange, Robin; Peternier, Achille

doi:10.1109/innovations.2008.4781778

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…In addition, we will develop a user interface to allow a better control of the simulation. This will also include interactive visualization of the molecules in the simulation ( de Heras Ciechomski et al , 2008 ; Falk et al , 2010 ).…”

Section: Discussionmentioning

confidence: 99%

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Hybrid spatial Gillespie and particle tracking simulation

2012

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Motivation: Cellular signal transduction involves spatial–temporal dynamics and often stochastic effects due to the low particle abundance of some molecular species. Others can, however, be of high abundances. Such a system can be simulated either with the spatial Gillespie/Stochastic Simulation Algorithm (SSA) or Brownian/Smoluchowski dynamics if space and stochasticity are important. To combine the accuracy of particle-based methods with the superior performance of the SSA, we suggest a hybrid simulation.Results: The proposed simulation allows an interactive or automated switching for regions or species of interest in the cell. Especially we see an application if for instance receptor clustering at the membrane is modeled in detail and the transport through the cytoplasm is included as well. The results show the increase in performance of the overall simulation, and the limits of the approach if crowding is included. Future work will include the development of a GUI to improve control of the simulation.Availability of Implementation: www.bison.ethz.ch/research/spatial_simulations.Contact: mklann@ee.ethz.ch or koeppl@ethz.chSupplementary/Information: Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

“…Note that the steps (1)–(4) could be executed in any order. Visualization of the particles/volumes was done with BioInspire from ScienceVisuals, Lausanne, Switzerland ( de Heras Ciechomski et al , 2008 ) and POVray ( www.povray.org ).…”

Section: Algorithmmentioning

confidence: 99%

Hybrid spatial Gillespie and particle tracking simulation

2012

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“…Filamentous structure visualization is used in several research fields, such as neuroscience [dHCMP08], flow visualization [ZSH96], vessel visualization [OP05], and DTI [MSE*06]. In general, the developed methods try to find a compromise between speed and quality [MPSS05].…”

Section: Related Workmentioning

confidence: 99%

Effective Techniques to Visualize Filament‐Surface Relationships

Kuß

Gensel

Meyer

et al. 2010

Computer Graphics Forum

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Combined visualizations of filamentous structures and surrounding volumetric objects are common in biological and medical applications. Often, the structures' spatial relationships remain unclear to the viewer. In this paper, we discuss and evaluate techniques to emphasize spatial relationships. We concentrate on the visualization of transparent objects and intersecting lines. Among various techniques, participants of an exploratory user study preferred coloring of lines, marking of line-surface intersections by glyphs, and the combination of both. These techniques were additionally evaluated in a confirmatory study in which participants were asked to judge whether a filament runs through a transparent structure. We found that the evaluated techniques significantly improve the participants' performance in terms of the number of correct responses and response time. The best performance was found for the combination of line coloring and intersection glyph display.

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“…Several approaches for the 3D visualization of neurons based on microscopy data have been presented [32,23,3,40]. Rendering of pure geometric representations of large neural networks has been addressed recently by de Heras Ciechomski et al [12].…”

Section: Related Workmentioning

confidence: 99%

BrainGazer - Visual Queries for Neurobiology Research

Brückner

Šoltészová

Gröller

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Neural projections in the brain of the fruit fly visualized using the BrainGazer system. Abstract-Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly Drosophila melanogaster, have proven to be an important tool in this research. Large databases of transgenic specimens are being built and need to be analyzed to establish models of neural information processing. In this paper we present an approach for the exploration and analysis of neural circuits based on such a database. We have designed and implemented BrainGazer, a system which integrates visualization techniques for volume data acquired through confocal microscopy as well as annotated anatomical structures with an intuitive approach for accessing the available information. We focus on the ability to visually query the data based on semantic as well as spatial relationships. Additionally, we present visualization techniques for the concurrent depiction of neurobiological volume data and geometric objects which aim to reduce visual clutter. The described system is the result of an ongoing interdisciplinary collaboration between neurobiologists and visualization researchers.

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Two-Phased Real-Time Rendering of Large Neuron Databases

Cited by 5 publications

References 3 publications

Hybrid spatial Gillespie and particle tracking simulation

Hybrid spatial Gillespie and particle tracking simulation

Effective Techniques to Visualize Filament‐Surface Relationships

BrainGazer - Visual Queries for Neurobiology Research

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