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Open Access

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

J.R. ANDERSON

Department of Ophthalmology, Moran Eye Center, University of Utah, Salt Lake City, Utah, U.S.A.

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S. MOHAMMED

Department of Ophthalmology, Moran Eye Center, University of Utah, Salt Lake City, Utah, U.S.A.

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B. GRIMM

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, U.S.A.

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B.W. JONES

Department of Ophthalmology, Moran Eye Center, University of Utah, Salt Lake City, Utah, U.S.A.

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P. KOSHEVOY

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, U.S.A.

Sorenson Media, Salt Lake City, Utah, U.S.A.

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T. TASDIZEN

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, U.S.A.

Department Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, U.S.A.

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R. WHITAKER

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, U.S.A.

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R.E. MARC

Department of Ophthalmology, Moran Eye Center, University of Utah, Salt Lake City, Utah, U.S.A.

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First published: 10 December 2010
Cited by: 27
James Anderson, Department of Ophthalmology, Moran Eye Center, University of Utah, Salt Lake City, UT 84132, U.S.A. Tel: +1‐801‐585‐6501; fax: +1‐801‐587‐7724; e‐mail: james.r.anderson@utah.edu

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Summary

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.

Number of times cited: 27

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