Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities.
Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities.
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医学图像分析框架的接口和集成：挑战和机遇。
DOI：
10.1109/bsec.2010.5510850
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Landman,BennettA
中科院分区：
文献类型：
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作者：
Covington,Kelsie;McCreedy,EvanS;Chen,Min;Carass,Aaron;Aucoin,Nicole;Landman,BennettA
医学成像的临床研究通常涉及大规模数据分析，以及在处理工作流程中捆绑在一起的相互依赖的软件工具集。有许多互补的平台可用，但这些平台在工作流程或数据格式方面并不容易兼容。图像科学家和临床研究人员都可以从使用最适合当前特定问题的框架中受益，但务实的选择通常要求使用折衷的平台进行协作。通过仔细调整的脚本来手动合并平台是有效的，但非常耗时，并且对于大规模集成工作来说是不可行的。因此，创新的好处受到平台依赖的限制。通过将算法从一个框架集成到另一个框架中来消除这一限制是这项工作的重点。我们提出并演示了一个轻量级接口系统，以跨平台公开参数并提供无缝集成。在这项初步工作中，我们重点关注四个平台：医学图像分析和可视化 (MIPAV)、Java 图像科学工具包 (JIST)、命令行工具和 3D 切片器。我们探讨了三个案例研究：(1) 为 MIPAV 提供一个系统来公开内部算法并在 JIST 中利用这些算法，(2) 通过自记录命令行界面公开 JIST 模块以包含在脚本环境中，以及 (3) 在 3D Slicer 中检测和使用 JIST 模块。我们回顾了开发语言（例如 MIPAV 和 JIST 中的 Java）和跨语言（例如 3D Slicer 中的 C/C++ 和命令行工具中的 shell）内轻量级软件集成的挑战和机遇。
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).