Abstract
PurposeThis 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.MethodsAt 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.ResultsUsing 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.ConclusionThe 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.
Highlights
The development of novel algorithms for image-guided interventions (IGI) brings together research in six areas: medical imaging, medical image computing, registration, tracking, visualisation, and user interface design
We have found it increasingly hard to recruit researchers with the skills or willingness to develop in C++, making development and translation based on these platforms more difficult
We have presented the SciKit-Surgery libraries, a set of largely stand-alone libraries to support research innovation and translation in surgical navigation
Summary
The development of novel algorithms for image-guided interventions (IGI) brings together research in six areas: medical imaging, medical image computing, registration, tracking, visualisation, and user interface design. Researchers aiming to build and test clinical applications incorporating novel algorithms can benefit significantly by using software platforms or toolkits that provide ready-made functionality in each of these six areas and the connections between them [23]. The Medical Imaging Interaction Toolkit (MITK) [9] and 3DSlicer [20] are the two most widely used open-source platforms for development of IGI systems. The Image-Guided Surgery Toolkik (IGSTK) [5] implemented many tools for imageguided interventions and could be integrated with MITK [16] and 3DSlicer; IGSTK is no longer under development. Other platforms include CamiTK [8], NifTK [3], IBIS [7], CISST [4], and CustusX [1]
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