Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers

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dc.contributor.authorAbdellah, Marwanen_US
dc.contributor.authorFavreau, Cyrilleen_US
dc.contributor.authorHernando, Juanen_US
dc.contributor.authorLapere, Samuelen_US
dc.contributor.authorSchürmann, Felixen_US
dc.contributor.editorVidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.en_US
dc.date.accessioned2019-09-11T05:09:00Z
dc.date.available2019-09-11T05:09:00Z
dc.date.issued2019
dc.description.abstractWe present the results of exploring the capabilities of skinning modifiers to generate high fidelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain specific framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neuroscientific community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientific illustrations of in silico neuronal circuits for media production with physically-based path tracers.en_US
dc.description.sectionheadersVirtual Reality
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20191257
dc.identifier.isbn978-3-03868-096-3
dc.identifier.pages45-53
dc.identifier.urihttps://doi.org/10.2312/cgvc.20191257
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20191257
dc.publisherThe Eurographics Associationen_US
dc.titleGenerating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiersen_US
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