Next Generation of the JAVA Image Science Toolkit (JIST) Visualization and Validation
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3378
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 disk. 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, despite changes in the underlying architecture. These three “next 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.
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minus Next Generation of the JAVA Image Science Toolkit(JIST) Visualization and Validation by Jung-san Lee on 2013-01-22 21:57:49 for revision #1
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Summary:


This paper proposes the next generation for Java Image Science Toolkit (JIST), which realize the visualization function and validation for the integrated medical image processing platform to ease the whole medical image process, and analytically prevents the inaccuracy from degrading the performance of open-source development.  


Hypothesis:

Finite medical imaging processing algorithms components. 

Evidence:

Different medical imaging processing pipelines are formed to process different medical image data and realize several complex algorithems cross platforms.

Open Science:

Yes.

Reproducibility:

Yes.


The source code compiles and runs well.

Use of Open Source Software:

The authors not only describe the open source advantages, but also provide the open source nightly build validation workflow with Hudson. 

Open source Contributions:

Yes.


The code is clear and easy to run.

Code Quality :

The code is well written and suitable for multiple-platforms.

Quality of the data :

The data provided is usable and make sense.

Interest:

More imaging processing algorithms could be developped within this tool.

Free comment :

The platform is flexible on different operating systems, and the pipeline is reusable and designable for researchers to implement their own algorithms with specific data as well as handling various data analyses. The paper is well organized with strong theoretical results and comprehensive evaluation studies with real traces collected from multiple complex medical image processing algorithms and neuroimage data.

It is also desired that the authors provide some discussions of the open-source challenges and opportunities and offer the solutions with Hudson to realize the nightly build validation analysis.

The Code attached works well and the open-source platform works efficiently with helps of Hudson nightly build procedures, which provide a nice developing environment for the developers for JIST.

Overall, the paper is well organized with strong theoretical results and comprehensive evaluation studies on the clinic neuroimage dataset.



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Categories: Data Representation, Programming
Keywords: JIST next generation, medical image analysis, visualization, fidelity, continuous integration
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