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Comprehensive protocol for mixed reality visualization and navigation using 3D Slicer.

Ziyu Qi1, Christopher Nimsky1,2, Miriam H A Bopp1,2

  • 1Department of Neurosurgery, University of Marburg, Baldingerstrasse, Marburg, Germany.

Plos One
|March 31, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a reproducible lab protocol for mixed reality navigation (MRN) in neurosurgery using open-source software. It enables accurate visualization and spatial mapping for improved surgical outcomes.

Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Computer-Aided Surgery

Background:

  • Mixed Reality (MxR) enhances neurosurgical navigation, offering improved visualization and spatial accuracy.
  • MxR navigation (MRN) presents a cost-effective alternative to conventional systems but faces implementation challenges.
  • A need exists for structured workflows and rigorous accuracy assessments for MRN systems.

Purpose of the Study:

  • To present a structured, modular, and reproducible lab protocol for implementing and evaluating MRN systems in neurosurgery.
  • To detail workflows for both visualization and navigation-oriented tasks.
  • To ensure broad accessibility and reproducibility through open-source tools.

Main Methods:

  • Utilized the open-source platform 3D Slicer for protocol development.

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  • Included preprocessing steps: anonymization, quality checks, and multimodal image fusion.
  • Employed semi-automatic segmentation for anatomical structures and generation of 3D surface models (STL/OBJ).
  • Integrated parameterization of fiducial landmarks, anatomical surfaces, and laser projections for virtual-to-physical registration.
  • Conducted accuracy and performance validation using static digital twins (virtual and physical).

    • Main Results:

    • Defined sequential steps, inputs, and outputs for MRN workflows.
    • Generated precise 3D surface models and enabled accurate virtual-to-physical registration.
    • Provided quantitative displacement analyses and intuitive visualizations for clinical interpretation.
    • Demonstrated the protocol's modularity, reproducibility, and flexibility.

    Conclusions:

    • The developed protocol facilitates effective implementation and evaluation of MRN systems in neurosurgery.
    • Reliance on open-source software ensures interdisciplinary accessibility and reproducibility.
    • The protocol supports diverse users with basic knowledge in anatomy, neurosurgery, and computer graphics.