Ssecrett and neuroTrace: Interactive visualization and analysis tools for large-scale neuroscience data sets

Handle URI:
http://hdl.handle.net/10754/561469
Title:
Ssecrett and neuroTrace: Interactive visualization and analysis tools for large-scale neuroscience data sets
Authors:
Jeong, Wonki; Beyer, Johanna; Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Blue, Rusty; Law, Charles; Vázquez Reina, Amelio; Reid, Rollie Clay; Lichtman, Jeff W M D; Pfister, Hanspeter
Abstract:
Recent 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Visual Computing Center (VCC)
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Computer Graphics and Applications
Issue Date:
May-2010
DOI:
10.1109/MCG.2010.56
PubMed ID:
20650718
PubMed Central ID:
PMC2909612
Type:
Article
ISSN:
02721716
Additional Links:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909612
Appears in Collections:
Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJeong, Wonkien
dc.contributor.authorBeyer, Johannaen
dc.contributor.authorHadwiger, Markusen
dc.contributor.authorBlue, Rustyen
dc.contributor.authorLaw, Charlesen
dc.contributor.authorVázquez Reina, Amelioen
dc.contributor.authorReid, Rollie Clayen
dc.contributor.authorLichtman, Jeff W M Den
dc.contributor.authorPfister, Hanspeteren
dc.date.accessioned2015-08-02T09:12:10Zen
dc.date.available2015-08-02T09:12:10Zen
dc.date.issued2010-05en
dc.identifier.issn02721716en
dc.identifier.pmid20650718en
dc.identifier.doi10.1109/MCG.2010.56en
dc.identifier.urihttp://hdl.handle.net/10754/561469en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909612en
dc.subjectComputer graphicsen
dc.subjectConnectomeen
dc.subjectGraphics and multimediaen
dc.subjectGraphics hardwareen
dc.subjectImplicit surface renderingen
dc.subjectNeuroscienceen
dc.subjectSegmentationen
dc.subjectVolume renderingen
dc.titleSsecrett and neuroTrace: Interactive visualization and analysis tools for large-scale neuroscience data setsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journalIEEE Computer Graphics and Applicationsen
dc.identifier.pmcidPMC2909612en
dc.contributor.institutionHarvard University Initiative in Innovative Computing, School of Engineering and Applied Science, United Statesen
kaust.authorBeyer, Johannaen
kaust.authorHadwiger, Markusen

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