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SciKit-Surgery: compact libraries for surgical navigation.

Stephen Thompson1, Thomas Dowrick2, Mian Ahmad2

  • 1Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK. s.thompson@ucl.ac.uk.

International Journal of Computer Assisted Radiology and Surgery
|May 22, 2020
PubMed
Summary
This summary is machine-generated.

SciKit-Surgery libraries accelerate clinical application development for image-guided interventions. These modular Python tools simplify complex software translation, enabling faster clinical trials and wider research dissemination.

Keywords:
Image-guided surgeryPlatformPythonSoftwareSurgical navigation

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Area of Science:

  • Medical technology
  • Software engineering
  • Image-guided interventions

Background:

  • Developing clinical applications for image-guided interventions requires robust and adaptable software solutions.
  • Existing platforms can hinder rapid translation from research to clinical practice.
  • There is a need for efficient tools to support the progression of surgical trials.

Purpose of the Study:

  • Introduce SciKit-Surgery, a suite of libraries for rapid development of clinical applications in image-guided interventions.
  • Facilitate the translation of research software to production environments without reimplementation.
  • Aim to support the transition from single-surgeon to multicenter trials within two years.

Main Methods:

  • SciKit-Surgery comprises 13 stand-alone, open-source Python libraries.
  • Libraries offer functionality for visualization, augmented reality, and hardware interfaces (video, tracking, ultrasound).
  • Design emphasizes modularity, orthogonality, and robust testing for fast development and translation.

Main Results:

  • Analysis of example applications shows SciKit-Surgery supports rapid development of testable clinical applications.
  • Stricter orthogonality between libraries reduces code complexity and cross-dependencies.
  • SciKit-Surgery demonstrates potential for wider dissemination of novel surgical research.

Conclusions:

  • SciKit-Surgery leverages Python's modularity and NumPy for an extensible, well-tested toolkit.
  • Applications built with SciKit-Surgery exhibit simpler dependency structures compared to monolithic platforms.
  • This facilitates more feasible and efficient clinical translation of image-guided intervention technologies.