ConnectomeExplorer: Query-guided visual analysis of large volumetric neuroscience data

Handle URI:
http://hdl.handle.net/10754/563124
Title:
ConnectomeExplorer: Query-guided visual analysis of large volumetric neuroscience data
Authors:
Beyer, Johanna; Al-Awami, Ali K. ( 0000-0002-8725-1958 ) ; Kasthuri, Narayanan; Lichtman, Jeff W M D; Pfister, Hanspeter; Hadwiger, Markus ( 0000-0003-1239-4871 )
Abstract:
This paper presents ConnectomeExplorer, an application for the interactive exploration and query-guided visual analysis of large volumetric electron microscopy (EM) data sets in connectomics research. Our system incorporates a knowledge-based query algebra that supports the interactive specification of dynamically evaluated queries, which enable neuroscientists to pose and answer domain-specific questions in an intuitive manner. Queries are built step by step in a visual query builder, building more complex queries from combinations of simpler queries. Our application is based on a scalable volume visualization framework that scales to multiple volumes of several teravoxels each, enabling the concurrent visualization and querying of the original EM volume, additional segmentation volumes, neuronal connectivity, and additional meta data comprising a variety of neuronal data attributes. We evaluate our application on a data set of roughly one terabyte of EM data and 750 GB of segmentation data, containing over 4,000 segmented structures and 1,000 synapses. We demonstrate typical use-case scenarios of our collaborators in neuroscience, where our system has enabled them to answer specific scientific questions using interactive querying and analysis on the full-size data for the first time. © 1995-2012 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 Transactions on Visualization and Computer Graphics
Issue Date:
Dec-2013
DOI:
10.1109/TVCG.2013.142
PubMed ID:
24051854
PubMed Central ID:
PMC4296725
Type:
Article
ISSN:
10772626
Sponsors:
We thank Thomas Theussl and Jose Conchello. This project was partially supported by the Intel ISTC-VC, Google, and NVIDIA.
Additional Links:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296725
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.authorBeyer, Johannaen
dc.contributor.authorAl-Awami, Ali K.en
dc.contributor.authorKasthuri, Narayananen
dc.contributor.authorLichtman, Jeff W M Den
dc.contributor.authorPfister, Hanspeteren
dc.contributor.authorHadwiger, Markusen
dc.date.accessioned2015-08-03T11:36:20Zen
dc.date.available2015-08-03T11:36:20Zen
dc.date.issued2013-12en
dc.identifier.issn10772626en
dc.identifier.pmid24051854en
dc.identifier.doi10.1109/TVCG.2013.142en
dc.identifier.urihttp://hdl.handle.net/10754/563124en
dc.description.abstractThis paper presents ConnectomeExplorer, an application for the interactive exploration and query-guided visual analysis of large volumetric electron microscopy (EM) data sets in connectomics research. Our system incorporates a knowledge-based query algebra that supports the interactive specification of dynamically evaluated queries, which enable neuroscientists to pose and answer domain-specific questions in an intuitive manner. Queries are built step by step in a visual query builder, building more complex queries from combinations of simpler queries. Our application is based on a scalable volume visualization framework that scales to multiple volumes of several teravoxels each, enabling the concurrent visualization and querying of the original EM volume, additional segmentation volumes, neuronal connectivity, and additional meta data comprising a variety of neuronal data attributes. We evaluate our application on a data set of roughly one terabyte of EM data and 750 GB of segmentation data, containing over 4,000 segmented structures and 1,000 synapses. We demonstrate typical use-case scenarios of our collaborators in neuroscience, where our system has enabled them to answer specific scientific questions using interactive querying and analysis on the full-size data for the first time. © 1995-2012 IEEE.en
dc.description.sponsorshipWe thank Thomas Theussl and Jose Conchello. This project was partially supported by the Intel ISTC-VC, Google, and NVIDIA.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296725en
dc.subjectConnectomicsen
dc.subjectneuroscienceen
dc.subjectpetascale volume analysisen
dc.subjectquery algebraen
dc.subjectvisual knowledge discoveryen
dc.titleConnectomeExplorer: Query-guided visual analysis of large volumetric neuroscience dataen
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 Transactions on Visualization and Computer Graphicsen
dc.identifier.pmcidPMC4296725en
dc.contributor.institutionCenter for Brain Science, Harvard University, United Statesen
kaust.authorBeyer, Johannaen
kaust.authorAl-Awami, Ali K.en
kaust.authorHadwiger, Markusen

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