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The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets

Modern microscope automation permits the collection of vast amounts of continuous anatomical imagery in both two and three dimensions. These large data sets present significant challenges for data storage, access, viewing, annotation and analysis. The cost and overhead of collecting and storing the...

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Detalles Bibliográficos
Autores principales: ANDERSON, JR, MOHAMMED, S, GRIMM, B, JONES, BW, KOSHEVOY, P, TASDIZEN, T, WHITAKER, R, MARC, RE
Formato: Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3017751/
https://www.ncbi.nlm.nih.gov/pubmed/21118201
http://dx.doi.org/10.1111/j.1365-2818.2010.03402.x
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author ANDERSON, JR
MOHAMMED, S
GRIMM, B
JONES, BW
KOSHEVOY, P
TASDIZEN, T
WHITAKER, R
MARC, RE
author_facet ANDERSON, JR
MOHAMMED, S
GRIMM, B
JONES, BW
KOSHEVOY, P
TASDIZEN, T
WHITAKER, R
MARC, RE
author_sort ANDERSON, JR
collection PubMed
description Modern microscope automation permits the collection of vast amounts of continuous anatomical imagery in both two and three dimensions. These large data sets present significant challenges for data storage, access, viewing, annotation and analysis. The cost and overhead of collecting and storing the data can be extremely high. Large data sets quickly exceed an individual's capability for timely analysis and present challenges in efficiently applying transforms, if needed. Finally annotated anatomical data sets can represent a significant investment of resources and should be easily accessible to the scientific community. The Viking application was our solution created to view and annotate a 16.5 TB ultrastructural retinal connectome volume and we demonstrate its utility in reconstructing neural networks for a distinctive retinal amacrine cell class. Viking has several key features. (1) It works over the internet using HTTP and supports many concurrent users limited only by hardware. (2) It supports a multi-user, collaborative annotation strategy. (3) It cleanly demarcates viewing and analysis from data collection and hosting. (4) It is capable of applying transformations in real-time. (5) It has an easily extensible user interface, allowing addition of specialized modules without rewriting the viewer.
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spelling pubmed-30177512011-01-19 The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets ANDERSON, JR MOHAMMED, S GRIMM, B JONES, BW KOSHEVOY, P TASDIZEN, T WHITAKER, R MARC, RE J Microsc Original Articles Modern microscope automation permits the collection of vast amounts of continuous anatomical imagery in both two and three dimensions. These large data sets present significant challenges for data storage, access, viewing, annotation and analysis. The cost and overhead of collecting and storing the data can be extremely high. Large data sets quickly exceed an individual's capability for timely analysis and present challenges in efficiently applying transforms, if needed. Finally annotated anatomical data sets can represent a significant investment of resources and should be easily accessible to the scientific community. The Viking application was our solution created to view and annotate a 16.5 TB ultrastructural retinal connectome volume and we demonstrate its utility in reconstructing neural networks for a distinctive retinal amacrine cell class. Viking has several key features. (1) It works over the internet using HTTP and supports many concurrent users limited only by hardware. (2) It supports a multi-user, collaborative annotation strategy. (3) It cleanly demarcates viewing and analysis from data collection and hosting. (4) It is capable of applying transformations in real-time. (5) It has an easily extensible user interface, allowing addition of specialized modules without rewriting the viewer. Blackwell Publishing Ltd 2011-01 /pmc/articles/PMC3017751/ /pubmed/21118201 http://dx.doi.org/10.1111/j.1365-2818.2010.03402.x Text en Journal compilation © 2011 Royal Microscopical Society http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
spellingShingle Original Articles
ANDERSON, JR
MOHAMMED, S
GRIMM, B
JONES, BW
KOSHEVOY, P
TASDIZEN, T
WHITAKER, R
MARC, RE
The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
title The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
title_full The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
title_fullStr The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
title_full_unstemmed The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
title_short The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
title_sort viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3017751/
https://www.ncbi.nlm.nih.gov/pubmed/21118201
http://dx.doi.org/10.1111/j.1365-2818.2010.03402.x
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