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The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software

Overview of attention for article published in Neuroinformatics, January 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

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1 patent
wikipedia
2 Wikipedia pages

Citations

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120 Dimensions

Readers on

mendeley
90 Mendeley
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3 CiteULike
Title
The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software
Published in
Neuroinformatics, January 2010
DOI 10.1007/s12021-009-9061-2
Pubmed ID
Authors

Blake C. Lucas, John A. Bogovic, Aaron Carass, Pierre-Louis Bazin, Jerry L. Prince, Dzung L. Pham, Bennett A. Landman

Abstract

Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and connectivity of biological mechanisms. With these advanced methods comes a heavy reliance on computer-based processing, analysis and interpretation. While the neuroimaging community has produced many excellent academic and commercial tool packages, new tools are often required to interpret new modalities and paradigms. Developing custom tools and ensuring interoperability with existing tools is a significant hurdle. To address these limitations, we present a new framework for algorithm development that implicitly ensures tool interoperability, generates graphical user interfaces, provides advanced batch processing tools, and, most importantly, requires minimal additional programming or computational overhead. Java-based rapid prototyping with this system is an efficient and practical approach to evaluate new algorithms since the proposed system ensures that rapidly constructed prototypes are actually fully-functional processing modules with support for multiple GUI's, a broad range of file formats, and distributed computation. Herein, we demonstrate MRI image processing with the proposed system for cortical surface extraction in large cross-sectional cohorts, provide a system for fully automated diffusion tensor image analysis, and illustrate how the system can be used as a simulation framework for the development of a new image analysis method. The system is released as open source under the Lesser GNU Public License (LGPL) through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 7%
Netherlands 2 2%
United Kingdom 2 2%
Spain 1 1%
Brazil 1 1%
Unknown 78 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 21%
Researcher 17 19%
Student > Master 10 11%
Professor > Associate Professor 7 8%
Student > Bachelor 6 7%
Other 22 24%
Unknown 9 10%
Readers by discipline Count As %
Engineering 20 22%
Computer Science 15 17%
Medicine and Dentistry 15 17%
Agricultural and Biological Sciences 7 8%
Physics and Astronomy 7 8%
Other 13 14%
Unknown 13 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2021.
All research outputs
#5,611,796
of 26,017,215 outputs
Outputs from Neuroinformatics
#101
of 436 outputs
Outputs of similar age
#31,082
of 180,281 outputs
Outputs of similar age from Neuroinformatics
#1
of 2 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 436 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 180,281 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them