SciKit-Surgery: compact libraries for surgical navigation
SciKit-Surgery: compact libraries for surgical navigation
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DOI：
10.1007/s11548-020-02180-5
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发表时间：
2020-05-20
影响因子：
3
通讯作者：
Clarkson, Matthew J.
中科院分区：
文献类型：
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作者：
Thompson, Stephen;Dowrick, Thomas;Clarkson, Matthew J.
目的：本文介绍了本研究中设计的SciKit-Surgery资料库，旨在使影像引导介入的临床应用得以快速发展。SciKit-Surgery实现了一系列紧凑、正交的库，并伴随着强大的测试、文档和质量控制。SciKit-Surgery文库可以快速组装成可测试的临床应用程序，并随后转换为生产软件，而不需要重新实现软件。其目标是支持在两年内从单一外科医生试验转变为多中心试验。方法在出版时，有13个SciKit-Surgery图书馆提供外科可视化和增强现实的功能，以及用于视频、跟踪和超声源的硬件接口。这些库是独立的、开放源码的，并提供了Python接口。这种设计方法支持快速开发健壮的应用程序和后续翻译。本文将这些库与现有的平台进行了比较，并使用了两个示例应用程序来展示SciKit-Surgery库如何在实践中使用。结果使用代码行数和交叉依赖的出现情况作为代码复杂性的替代度量，分析了使用SciKit-Surgery库的两个示例应用程序。SciKit-Surgery库展示了支持快速开发可测试的临床应用程序的能力。通过维护库之间更严格的正交性，可以减少依赖项的数量和复杂性。SciKit-Surgery图书馆还展示了支持新研究更广泛传播的潜力。结论SciKit-Surgery库利用了Python语言的模块化和NumPy包的标准数据类型，为图像引导介入应用程序的开发提供了一套易于使用、经过良好测试和可扩展的工具。与使用单一平台构建的相同应用程序相比，基于SciKit-Surgery构建的示例应用程序具有更简单的依赖结构，从而使正在进行的临床翻译更加可行。
Purpose This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years. Methods At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice. Results Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research. Conclusion The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.