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dc.contributor.authorJeong, Wonki
dc.contributor.authorBeyer, Johanna
dc.contributor.authorHadwiger, Markus
dc.contributor.authorBlue, Rusty
dc.contributor.authorLaw, Charles
dc.contributor.authorVázquez Reina, Amelio
dc.contributor.authorReid, Rollie Clay
dc.contributor.authorLichtman, Jeff W M D
dc.contributor.authorPfister, Hanspeter
dc.date.accessioned2015-08-02T09:12:10Z
dc.date.available2015-08-02T09:12:10Z
dc.date.issued2010-05-06
dc.identifier.issn02721716
dc.identifier.pmid20650718
dc.identifier.doi10.1109/MCG.2010.56
dc.identifier.urihttp://hdl.handle.net/10754/561469
dc.description.abstractRecent advances in optical and electron microscopy let scientists acquire extremely high-resolution images for neuroscience research. Data sets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible, interactive tools, the authors introduce Ssecrett and NeuroTrace, two tools they designed for interactive exploration and analysis of large-scale optical- and electron-microscopy images to reconstruct complex neural circuits of the mammalian nervous system. © 2010 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909612
dc.relation.urlhttp://europepmc.org/articles/pmc2909612?pdf=render
dc.rights(c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.rightsThis file is an open access version redistributed from: http://europepmc.org/articles/pmc2909612?pdf=render
dc.subjectComputer graphics
dc.subjectConnectome
dc.subjectGraphics and multimedia
dc.subjectGraphics hardware
dc.subjectImplicit surface rendering
dc.subjectNeuroscience
dc.subjectSegmentation
dc.subjectVolume rendering
dc.titleSsecrett and neuroTrace: Interactive visualization and analysis tools for large-scale neuroscience data sets
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalIEEE Computer Graphics and Applications
dc.identifier.pmcidPMC2909612
dc.eprint.versionPost-print
dc.contributor.institutionHarvard University Initiative in Innovative Computing, School of Engineering and Applied Science, United States
kaust.personBeyer, Johanna
kaust.personHadwiger, Markus
refterms.dateFOA2020-07-01T13:20:17Z


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