Medical imaging data can be gathered in such vast quantities that it is often a challenge to carry out appropriate processing steps despite publicly available tools that might already exist for carrying out many of the procedures. A tool that automatically handles formatting and compatibility issues while integrating software from a variety of platforms is essential. The neuroimaging community benefits from several, excellent pipeline tools and image processing libraries. These environments provide end-users with systems to process and visualize large-scale datasets. While there has been substantial convergence in terms of the desirable features in a successful system (e.g., grid processing, block diagrams), the Java Image Science Toolkit (JIST) uniquely address important challenges. Cross-platform compilation and deployment are notoriously difficult, but JIST is based on the Java programming language (Sun Microsystems, Santa Clara, CA), which is inherently cross-platform and able to run on almost any platform. To date, no other pipeline software has made native use of neuroimaging image processing libraries with Java. JIST enables a unique flavor of """"""""write-once, run many"""""""" development: programmers need only write a core section of code and users can access this functionality in many different ways (e.g., within block diagram, as a plugin, from the command line, from within Matlab, on a grid, etc.). Thus, JIST provide a seamless development path from prototype to cluster-based parallel processing. The proposed research and development effort will significantly improve the interoperability and adoptability of the JIST framework to enhance adoption by the broader neuroimaging research community. Specifically, this work will (1) enhance developers'ability to monitor and validate modular routines in collaborative development and (2) improve the usability through interactive visualization capabilities and detailed documentation.
These aims have been chosen to direct development efforts at the specific concerns of existing JIST users. The primary hypothesis of this proposal is that by addressing the specific concerns of current JIST users, the platform will be made more accessible to the broader neuroimaging community. In turn, JIST will provide a more significant contribution toward advances in clinical research. These developments will ease the learning curve and provide more intuitive and responsive experiences for both developers and users. Users will be able to more readily leverage the substantial image analysis capabilities already available within JIST and benefit from improved accessibility of advanced features.
The proposed research improves the interoperability and adoptability of the Java Image Science Toolkit (JIST) resource using the Neuroimaging Informatics Tools and Resources Clearinghouse infrastructure. This effort will provide a more intuitive and responsive experience for both developers and users through an improved user interface and an automated algorithm validation and testing system. The end result will be that clinical investigators and image scientists will be able to more readily leverage the substantial analysis capabilities already available within JIST.
| Huo, Yuankai; Bao, Shunxing; Parvathaneni, Prasanna et al. (2018) Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation. Proc SPIE Int Soc Opt Eng 10574: |
| Hu, Peijun; Huo, Yuankai; Kong, Dexing et al. (2018) Automated Characterization of Body Composition and Frailty with Clinically Acquired CT. Comput Methods Clin Appl Musculoskelet Imaging (2017) 10734:25-35 |
| Bermudez, Camilo; Plassard, Andrew J; Davis, Taylor L et al. (2018) Learning Implicit Brain MRI Manifolds with Deep Learning. Proc SPIE Int Soc Opt Eng 10574: |
| Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing et al. (2018) Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation. IEEE Trans Biomed Eng 65:336-343 |
| Chaganti, Shikha; Nelson, Katrina; Mundy, Kevin et al. (2018) Imaging biomarkers in thyroid eye disease and their clinical associations. J Med Imaging (Bellingham) 5:044001 |
| Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing et al. (2017) Multi-atlas Segmentation Enables Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly. Proc SPIE Int Soc Opt Eng 10133: |
| Liu, Jiaqi; Huo, Yuankai; Xu, Zhoubing et al. (2017) Multi-Atlas Spleen Segmentation on CT Using Adaptive Context Learning. Proc SPIE Int Soc Opt Eng 10133: |
| Huo, Yuankai; Resnick, Susan M; Landman, Bennett A (2017) 4D Multi-atlas Label Fusion using Longitudinal Images. Patch Based Tech Med Imaging (2017) 10530:3-11 |
| Huo, Yuankai; Asman, Andrew J; Plassard, Andrew J et al. (2017) Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp 38:599-616 |
| Xu, Zhoubing; Lee, Christopher P; Heinrich, Mattias P et al. (2016) Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT. IEEE Trans Biomed Eng 63:1563-72 |
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