Department of Diagnostic Radiology, Yale University, 300 Cedar Street, New Haven, CT 06520, USA.
Neuroinformatics. 2011 Mar;9(1):69-84. doi: 10.1007/s12021-010-9092-8.
Developing both graphical and command-line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software--BioImage Suite (bioimagesuite.org).
开发神经影像学算法的图形和命令行用户界面都需要大量的工作。只有当神经影像学算法能够被预期用户轻松频繁地使用时，它们才能发挥其潜力。在多个平台上部署大量此类算法需要用户界面控件的一致性、各种平台上的一致结果以及全面的测试。我们提出了一种新颖的面向对象框架的设计和实现，该框架允许使用许多可重复使用的组件和轻松添加图形用户界面控件来快速开发复杂的图像分析算法。我们的框架还允许对算法进行简化但强大的夜间测试，以确保稳定性和跨平台互操作性。所有功能都封装到一个软件对象中，不需要为用户界面、测试或部署单独的源代码。这种构建方式使我们的框架非常适合开发用于医学图像分析和计算机辅助干预的新颖、稳定和易于使用的算法。该框架已在耶鲁大学部署，并在开源多平台图像分析软件--BioImage Suite（bioimagesuite.org）中发布供公众使用。
