Tumor growth models to generate pathologies for surgical training simulators.

Abstract

Many virtual reality based surgical training simulators have been presented in the last few years. These systems promise to alleviate the lack of realistic training possibilities common to minimally invasive procedures. Virtual reality allows for riskless training on a wide range of findings in a condensed period of time. We investigated different methods for the generation of tumor models suitable for surgical training simulators. The goal of our research is a high fidelity hysteroscopy simulator which provides an individual surgical scene for every training. Emphasis was placed on the modeling of growth processes leading to the generation of macroscopically realistic findings of the most common pathologies in hysteroscopy, namely polyps and myomas found in the uterine cavity. Both a cellular automaton and a particle based tumor growth model are presented and discussed.