A Layered Model of a Virtual Human Intestine for Surgery Simulation

Laure France 1 Julien Lenoir 2, 3 Alexis Angelidis 4 P. Meseure 2, 3 Marie-Paule Cani 4 François Faure 4 Christophe Chaillou 2, 3
3 ALCOVE - Collaborative interactive virtual environment
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, IRCICA
4 EVASION - Virtual environments for animation and image synthesis of natural objects
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : In this paper, we propose a new approach to simulate the small intestine in a context of laparoscopic surgery. The ultimate aim of this work is to simulate the training of a basic surgical gesture in real-time: moving aside the intestine to reach hidden areas of the abdomen. The main problem posed by this kind of simulation is animating the intestine. The problem comes from the nature of the intestine: a very long tube which is not isotropically elastic, and is contained in a volume that is small when compared to the intestine's length. It coils extensively and collides with itself in many places. To do this, we use a layered model to animate the intestine. The intestine's axis is animated as a linear mechanical component. A specific sphere-based model han- dles contacts and self-collisions. A skinning model is used to create the intestine's volume around the axis. This paper discusses and compares three different repre- sentations for skinning the intestine: a parametric surface model and two implicit surface models. The first implicit surface model uses point skeletons while the sec- ond uses local convolution surfaces. Using these models, we obtained good-looking results in real-time.
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Article dans une revue
Medical Image Analysis, Elsevier, 2005
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  • HAL Id : inria-00394478, version 1

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Laure France, Julien Lenoir, Alexis Angelidis, P. Meseure, Marie-Paule Cani, et al.. A Layered Model of a Virtual Human Intestine for Surgery Simulation. Medical Image Analysis, Elsevier, 2005. 〈inria-00394478〉

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