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Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities

1Vanderbilt University, Department of Electrical Engineering, Nashville, TN, USA.
2National Institutes of Health, Center for Information Technology, Bethesda, MD, USA.
3Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD, USA.
4Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Annu ORNL Biomed Sci Eng Cent Conf
Volume Number:
Annu ORNL Biomed Sci Eng Cent Conf. 2010 May 25;2010:1-4.
PubMed ID:
Appears in Collections:
R21 NS064534/NS/NINDS NIH HHS/United States
Generated Citation:
Covington K., McCreedy E.S., Chen M., Carass A., Aucoin N., Landman B.A. Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities. Annu ORNL Biomed Sci Eng Cent Conf. 2010 May 25;2010:1-4. PMID: 21151892. PMCID: PMC2998761.
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Clinical research with medical imaging typically involves large-scale data analysis with interdependent software toolsets tied together in a processing workflow. Numerous, complementary platforms are available, but these are not readily compatible in terms of workflows or data formats. Both image scientists and clinical investigators could benefit from using the framework which is a most natural fit to the specific problem at hand, but pragmatic choices often dictate that a compromise platform is used for collaboration. Manual merging of platforms through carefully tuned scripts has been effective, but exceptionally time consuming and is not feasible for large-scale integration efforts. Hence, the benefits of innovation are constrained by platform dependence. Removing this constraint via integration of algorithms from one framework into another is the focus of this work. We propose and demonstrate a light-weight interface system to expose parameters across platforms and provide seamless integration. In this initial effort, we focus on four platforms Medical Image Analysis and Visualization (MIPAV), Java Image Science Toolkit (JIST), command line tools, and 3D Slicer. We explore three case studies: (1) providing a system for MIPAV to expose internal algorithms and utilize these algorithms within JIST, (2) exposing JIST modules through self-documenting command line interface for inclusion in scripting environments, and (3) detecting and using JIST modules in 3D Slicer. We review the challenges and opportunities for light-weight software integration both within development language (e.g., Java in MIPAV and JIST) and across languages (e.g., C/C++ in 3D Slicer and shell in command line tools).

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