KEYWORDS: Nerve, Visualization, Data modeling, Systems modeling, Computer simulations, 3D modeling, Process modeling, Virtual reality, Visual process modeling, Bone
This paper contributes to modeling, simulation and visualization of peripheral nerve cords. Until now, only
sparse datasets of nerve cords can be found. In addition, this data has not yet been used in simulators, because
it is only static. To build up a more flexible anatomical structure of peripheral nerve cords, we propose a
hierarchical tree data structure where each node represents a nerve branch. The shape of the nerve segments
itself is approximated by spline curves. Interactive modeling allows for the creation and editing of control
points which are used for branching nerve sections, calculating spline curves and editing spline representations
via cross sections. Furthermore, the control points can be attached to different anatomic structures. Through
this approach, nerve cords deform in accordance to the movement of the connected structures, e.g., muscles or
bones. As a result, we have developed an intuitive modeling system that runs on desktop computers and in
immersive environments. It allows anatomical experts to create movable peripheral nerve cords for articulated
virtual humanoids. Direct feedback of changes induced by movement or deformation is achieved by visualization
in real-time. The techniques and the resulting data are already used for medical simulators.
With a steadily increasing indication, regional anesthesia is still trained directly on the patient. To develop
a virtual reality (VR)-based simulation, a patient model is needed containing several tissues, which have to
be extracted from individual magnet resonance imaging (MRI) volume datasets. Due to the given modality
and the different characteristics of the single tissues, an adequate segmentation can only be achieved by using
a combination of segmentation algorithms. In this paper, we present a framework for creating an individual
model from MRI scans of the patient. Our work splits in two parts. At first, an easy-to-use and extensible tool
for handling the segmentation task on arbitrary datasets is provided. The key idea is to let the user create a
segmentation for the given subject by running different processing steps in a purposive order and store them in a
segmentation script for reuse on new datasets. For data handling and visualization, we utilize the Medical Imaging
Interaction Toolkit (MITK), which is based on the Visualization Toolkit (VTK) and the Insight Segmentation
and Registration Toolkit (ITK). The second part is to find suitable segmentation algorithms and respectively
parameters for differentiating the tissues required by the RA simulation. For this purpose, a fuzzy c-means
clustering algorithm combined with mathematical morphology operators and a geometric active contour-based
approach is chosen. The segmentation process itself aims at operating with minimal user interaction, and the
gained model fits the requirements of the simulation. First results are shown for both, male and female MRI of
the pelvis.
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