Introduction: The practice of pre- and intra-operative interactive visualization and modeling continues to grow as its value to clinical practice is augmented by new technologies, such as virtual and augmented reality, or 3D printing. Current tools which extract the necessary structural information from medical imaging modalities and allow virtual or other interrogation of the data are either difficult to use in a practical clinical setting, or sufficiently simple as to limit the knowledge available to the operator. Nonetheless, the broader medical visualization and simulation communities have invented tools which enable automated segmentation and interrogation of structures critical to the success of surgery, such as cranial nerves, vasculature, and cortical and subcortical parcellations.
Methods: We leverage these tools as inputs to a novel pipeline for neurosurgery simulation. Our pipeline is compatible with ATLAS-based subcortical volumetric segmentation (e.g., Freesurfer, ANTS), or any structural input in mesh- or voxel-based formats, together with volumetric data. The visualizer, based on VTK7’s OpenGL3x rendering backend, is efficient enough to display an arbitrary number of input structures or volumes at interactive refresh rates. Structures can be manipulated by adjusting parameters for each structure independently (e.g., color, opacity). Standard ATLAS-based and ITK/VTK-based tools are included in the pipeline directly. Also included is a novel volumetric shift-based segmentation tool, allowing an operating scientist to easily include information detailing aberrant pathologies rapidly and with minimal semantic information.
Results: We demonstrate these tools for a variety of cases, including tumor, vascular, hemorrhagic stroke, and spine. Its performance sufficient to run and be used on a laptop computer and capabilities for pre-operative planning through 3D printing the generated structures.
Conclusions: We find that repurposing the power of existing segmentation tools within a novel modular, multi-modal framework enables robust neurosurgical simulation for pre- and intra-operative planning with features not possible in any existing integrated simulation platform.
Patient Care: We have demonstrated through a variety of case studies that efficient, accessible, multi-modal simulation making use of all possible structural information about the individual case can dramatically improve pre-operative planning and intra-operative decision making in surgery. These improvements lead directly to patient outcome.
Learning Objectives: Pre-operative planning using virtual, augmented, and structural modeling.