NeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects

Handle URI:
http://hdl.handle.net/10754/575258
Title:
NeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects
Authors:
Al-Awami, Ali; Beyer, Johanna; Haehn, Daniel; Kasthuri, Narayanan; Lichtman, Jeff; Pfister, Hanspeter; Hadwiger, Markus ( 0000-0003-1239-4871 )
Abstract:
In the field of connectomics, neuroscientists acquire electron microscopy volumes at nanometer resolution in order to reconstruct a detailed wiring diagram of the neurons in the brain. The resulting image volumes, which often are hundreds of terabytes in size, need to be segmented to identify cell boundaries, synapses, and important cell organelles. However, the segmentation process of a single volume is very complex, time-intensive, and usually performed using a diverse set of tools and many users. To tackle the associated challenges, this paper presents NeuroBlocks, which is a novel visualization system for tracking the state, progress, and evolution of very large volumetric segmentation data in neuroscience. NeuroBlocks is a multi-user web-based application that seamlessly integrates the diverse set of tools that neuroscientists currently use for manual and semi-automatic segmentation, proofreading, visualization, and analysis. NeuroBlocks is the first system that integrates this heterogeneous tool set, providing crucial support for the management, provenance, accountability, and auditing of large-scale segmentations. We describe the design of NeuroBlocks, starting with an analysis of the domain-specific tasks, their inherent challenges, and our subsequent task abstraction and visual representation. We demonstrate the utility of our design based on two case studies that focus on different user roles and their respective requirements for performing and tracking the progress of segmentation and proofreading in a large real-world connectomics project.
KAUST Department:
Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
NeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects 2015:1 IEEE Transactions on Visualization and Computer Graphics
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Visualization and Computer Graphics
Issue Date:
12-Aug-2015
DOI:
10.1109/TVCG.2015.2467441
Type:
Article
ISSN:
1077-2626
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7192653
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Awami, Alien
dc.contributor.authorBeyer, Johannaen
dc.contributor.authorHaehn, Danielen
dc.contributor.authorKasthuri, Narayananen
dc.contributor.authorLichtman, Jeffen
dc.contributor.authorPfister, Hanspeteren
dc.contributor.authorHadwiger, Markusen
dc.date.accessioned2015-08-19T12:31:17Zen
dc.date.available2015-08-19T12:31:17Zen
dc.date.issued2015-08-12en
dc.identifier.citationNeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects 2015:1 IEEE Transactions on Visualization and Computer Graphicsen
dc.identifier.issn1077-2626en
dc.identifier.doi10.1109/TVCG.2015.2467441en
dc.identifier.urihttp://hdl.handle.net/10754/575258en
dc.description.abstractIn the field of connectomics, neuroscientists acquire electron microscopy volumes at nanometer resolution in order to reconstruct a detailed wiring diagram of the neurons in the brain. The resulting image volumes, which often are hundreds of terabytes in size, need to be segmented to identify cell boundaries, synapses, and important cell organelles. However, the segmentation process of a single volume is very complex, time-intensive, and usually performed using a diverse set of tools and many users. To tackle the associated challenges, this paper presents NeuroBlocks, which is a novel visualization system for tracking the state, progress, and evolution of very large volumetric segmentation data in neuroscience. NeuroBlocks is a multi-user web-based application that seamlessly integrates the diverse set of tools that neuroscientists currently use for manual and semi-automatic segmentation, proofreading, visualization, and analysis. NeuroBlocks is the first system that integrates this heterogeneous tool set, providing crucial support for the management, provenance, accountability, and auditing of large-scale segmentations. We describe the design of NeuroBlocks, starting with an analysis of the domain-specific tasks, their inherent challenges, and our subsequent task abstraction and visual representation. We demonstrate the utility of our design based on two case studies that focus on different user roles and their respective requirements for performing and tracking the progress of segmentation and proofreading in a large real-world connectomics project.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7192653en
dc.rights(c) 2015 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.en
dc.titleNeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projectsen
dc.typeArticleen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphicsen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Engineering and Applied Sciences at Harvard Universityen
dc.contributor.institutionNarayanan Kasthuri is with the School of Medicine at Boston Universityen
dc.contributor.institutionCenter for Brain Science at Harvard Universityen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorAi-Awami, Ali K.en
kaust.authorHadwiger, Markusen
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