Resource Development for the Java Image Science Toolkit

Java 图像科学工具包的资源开发

• 批准号: 8013701
• 负责人: Bennett A. Landman
• 金额: $ 14.7万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8013701
• 关键词: Resource; Development; Java; Image; Science

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

描述（由申请人提供）：医学成像数据可以大量收集，以至于尽管可能已经存在许多程序可以存在的公共工具，但仍要进行适当的处​​理步骤是一个挑战。 一种自动处理格式和兼容性问题的工具，同时从各种平台集成软件是必不可少的。 神经影像社区受益于多种出色的管道工具和图像处理库。 这些环境为最终用户提供了处理和可视化大规模数据集的系统。尽管成功的系统（例如，网格处理，框图）的理想特征已经存在实质性的融合，但Java Image Science Toolkit（JIST）独特地应对重要的挑战。 众所周知，跨平台汇编和部署很困难，但JIST基于Java编程语言（Sun Microsystems，Santa Clara，CA），该语言本质上是跨平台，并且能够在几乎任何平台上运行。迄今为止，尚无其他管道软件使用Java的神经成像图像处理库的本地使用。 JIST可以使“写入，运行许多”开发的独特风味：程序员只需要编写代码的核心部分，用户可以以许多不同的方式访问此功能（例如，在框图中，作为插件，从命令行，从matlab，从matlab，网格等上等）。因此，JIST提供了从原型到基于群集的并行处理的无缝开发路径。 拟议的研发工作将显着改善JIST框架的互操作性和可采用性，以增强更广泛的神经影像学研究所的采用。具体而言，这项工作将（1）增强开发人员在协作开发中监视和验证模块化例程的能力，以及（2）通过交互式可视化功能和详细文档来提高可用性。这些目标已被选为将开发工作指向现有JIST用户的具体问题。该提案的主要假设是，通过解决当前JIST用户的具体问题，该平台将使更广泛的神经成像社区更容易访问。反过来，JIST将为临床研究的进步提供更大的贡献。 这些发展将减轻学习曲线，并为开发人员和用户提供更直观和响应的体验。用户将能够更容易利用JIST内已经可用的实质图像分析功能，并受益于提高高级功能的可访问性。 公共卫生相关性：拟议的研究通过神经成像信息学工具和资源交换所基础架构来改善Java图像科学工具包（JIST）资源的互操作性和可采用性。这项工作将通过改进的用户界面以及自动化算法验证和测试系统为开发人员和用户提供更直观和响应的体验。最终结果将是临床研究人员和形象科学家将能够更容易利用JIST中已经可用的实质性分析功能。

项目成果

Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study.

腹疝 CT 腹壁自动分割：一项试点研究。

• DOI: 10.1117/12.2007060
• 发表时间: 2013
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Xu,Zhoubing;Allen,WadeM;Poulose,BenjaminK;Landman,BennettA
• 通讯作者: Landman,BennettA

Quantitative Anatomical Labeling of the Anterior Abdominal Wall.

腹前壁的定量解剖标记。

• DOI: 10.1117/12.2007071
• 发表时间: 2013
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Allen,WadeM;Xu,Zhoubing;Asman,AndrewJ;Poulose,BenjaminK;Landman,BennettA
• 通讯作者: Landman,BennettA

Robust Optic Nerve Segmentation on Clinically Acquired CT.

• DOI: 10.1117/12.2043715
• 发表时间: 2014-03-21
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Panda S;Asman AJ;Delisi MP;Mawn LA;Galloway RL;Landman BA
• 通讯作者: Landman BA

Texture analysis improves level set segmentation of the anterior abdominal wall.

• DOI: 10.1118/1.4828791
• 发表时间: 2013-12
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Zhoubing Xu;W. M. Allen;R. Baucom;B. Poulose;B. Landman