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A new submission has been added to the MIDAS Journal.

Title: Next Generation of the JAVA Image Science Toolkit (JIST) Visualization 
and Validation
Authors: Li B., Bryan F., Landman B.
Abstract: Modern medical imaging analyses often involve the concatenation of 
multiple steps, and neuroimaging analysis is no exception. The Java Image 
Science Toolkit (JIST) has provided a framework for both end users and 
engineers to synthesize processing modules into tailored, automatic multi-step 
processing pipelines (“layouts”) and rapid prototyping of module 
development. Since its release, JIST has facilitated substantial neuroimaging 
research and fulfilled much of its intended goal. However, key weaknesses must 
be addressed for JIST to more fully realize its potential and become accessible 
to an even broader community base. Herein, we identify three core challenges 
facing traditional JIST (JIST-I) and present the solutions in the next 
generation JIST (JIST-II). First, in response to community demand, we have 
introduced seamless data visualization; users can now click ‘show this 
data’ through the program interfaces and avoid the need to locating files on 
the dis!
 k. Second, as JIST is an open-source community effort by-design; any developer 
may add modules to the distribution and extend existing functionality for 
release. However, the large number of developers and different use cases 
introduced instability into the overall JIST-I framework, causing users to 
freeze on different, incompatible versions of JIST-I, and the JIST community 
began to fracture. JIST-II addresses the problem of compilation instability by 
performing continuous integration checks nightly to ensure community 
implemented changes do not negatively impact overall JIST-II functionality. 
Third, JIST-II allows developers and users to ensure that functionality is 
preserved by running functionality checks nightly using the continuous 
integration framework. With JIST-II, users can submit layout test cases and 
quality control criteria through a new GUI. These test cases capture all 
runtime parameters and help to ensure that the module produces results within 
tolerance, de!
 spite changes in the underlying architecture. These three “n!
 ext generation” improvements increase the fidelity of the JIST framework and 
enhance utility by allowing researchers to more seamlessly and robustly build, 
manage, and understand medical image analysis processing pipelines.

Download and review this publication at: http://hdl.handle.net/10380/3378

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