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.

中文翻译：

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SciKit-Surgery：用于手术导航的紧凑库。

目的 本文介绍了 SciKit-Surgery 库，旨在实现图像引导干预的临床应用的快速开发。SciKit-Surgery 实现了一系列紧凑的正交库，并伴随着强大的测试、文档和质量控制。SciKit-Surgery 库可以快速组装成可测试的临床应用程序，随后转换为生产软件，无需重新实现软件。其目的是支持在 2 年内从单一外科医生试验转化为多中心试验。方法 在出版时，有 13 个 SciKit-Surgery 库提供手术中的可视化和增强现实功能，以及用于视频、跟踪和超声源的硬件接口。这些库是独立的，开源的，并提供 Python 接口。这种设计方法可以快速开发强大的应用程序和后续翻译。本文将这些库与现有平台进行了比较，并使用两个示例应用程序来展示如何在实践中使用 SciKit-Surgery 库。结果 使用代码行数和交叉依赖的出现作为代码复杂性的代理测量，分析了两个使用 SciKit-Surgery 库的示例应用程序。SciKit-Surgery 库展示了支持可测试临床应用程序快速开发的能力。通过在库之间保持更严格的正交性，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更广泛传播新研究的潜力。结论 SciKit-Surgery 库利用 Python 语言的模块化和 NumPy 包的标准数据类型，为图像引导干预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. 基于 SciKit-Surgery 构建的示例应用程序具有比使用单一平台构建的相同应用程序更简单的依赖结构，使正在进行的临床翻译更加可行。