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High-fidelity surgical simulation |
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Our research group has been developing surgical simulation systems
in close collaboration with the OB/GYN Department of the University
Hospital Zurich for more than a decade. A key target of our work is
to achieve a high level of realism. We strive to go beyond rehearsal
of basic manipulative skills, and to enable the training of procedural
skills like decision making and problem solving. Furthermore, the
integration of the simulation systems into the medical curriculum is
tackled.
The driving medical application of our current activity is
the development of a surgical training simulator for hysteroscopy.
Several of my research foci are directly linked to this endeavor,
including real-time cutting of triangular and tetrahedral meshes,
generation of surgical training scenes, rendering of haptic feedback,
and real-time visualization of the hydrometra.
Further details are available on the webpage of the hysteroscopy simulator system - HystSim. |
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Multi-modal data segmentation |
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Extensive research has been invested in recent years into improving
interactive segmentation algorithms. However, the human computer
interface, a substantial part of an interactive setup, is usually
not investigated. The aim of this work is the optimal cooperation
between interactive image analysis algorithms and human operators.
A visuo-haptic interaction tool for medical segmentation has been
designed, which opens the way to virtual endoscopy of the small
intestine. The system has been installed and is used at the Radiology
Department of the University Hospital Zurich.
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Training scene generation |
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A key element of effective VR-based training is the ability to
generate variable scenarios. Due to this, adaptation of a trainee
to a specific scene can be avoided and natural variation, which is
encountered in most real life situations, can be included. This
research covers the main components needed to define a training
scene - the generation of the scene geometry, the modeling of
organ appearance, and the definition of biomechanical parameters.
The first element covers the derivation of the healthy
anatomy as well as typical pathological variations. The second
element deals with the synthesis of appropriate textures for
organ surfaces as well as the mapping of these to the previously
created geometries. The final element focuses on
techniques to set the biomechanical parameters describing the
deformation behavior of the soft tissue objects. We successfully
applied optimization-based techniques as well as analytical
derivations.
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Visuo-haptic colocated augmented reality |
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In this thread, we examine the integration of haptic
interfaces into augmented reality setups, focusing on the
combination of real and -- possibly indistinguishable --
virtual objects. The ultimate target of these endeavors
is the application of the framework to training of
manipulative skills in surgical environments. To this end,
highly accurate calibration, system stability, and low
latency are indispensable.
Moreover, in order
to allow simultaneous multi-modal interaction with real and
virtual objects, visuo-haptic collocation is also a
prerequisite. Therefore, we have developed a specialized
augmented reality environment, including hybrid tracking,
3D landmark refinement, robust occlusion detection,
accurate haptic device calibration, and a distributed
framework with low latency synchronization of system
components.
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Evaluation of soft tissue rendering |
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A central element of surgical simulators is the generation of
appropriate haptic feedback. Several factors influence this
rendering process, which could potentially degrade the feedback
quality. Among these are the selected mechanical
deformation model, tissue parameter setting, collision detection,
tool-tissue contact handling, as well as simulation and haptic
update rates. In this work we evaluated the overall fidelity of
the haptic rendering of soft tissue deformation using
multi-dimensional scaling analysis. The perceptual effects of
changes in rendering parameters could be quantified and
visualized.
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Real-time mesh cutting |
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A central training objective of virtual reality based surgical
simulation is the removal of pathologic tissue. This necessitates
stable, real-time updates of the underlying mesh representation.
We have developed a hybrid cutting approach for tetrahedral and
triangular meshes tailored to our hysteroscopic training system.
It combines topological update by subdivision with adjustments
of the existing topology. This is completed by a subsequent local
mesh optimization step.
Moreover, the mechanical and the visual
model are decoupled, thus allowing different resolutions of the
underlying mesh representations. An arbitrary, user-defined cut
surface can be closely approximated while avoiding the creation
of small or badly shaped elements, thus strongly reducing stability
problems in the subsequent deformation computation.
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