Virtual reality (VR)-based surgical simulator systems offer a very elegant approach to enriching and enhancing traditional training in endoscopic surgery. However, while a number of VR simulator systems have been proposed and realized in the past few years, most of these systems are far from being able to provide a reasonably realistic surgical environment. We explore the current limits for realism and the approaches to reaching and surpassing those limits by describing and analyzing the most important components of VR-based endoscopic simulators. The feasibility of the proposed techniques is demonstrated on a modular prototype system that implements the basic algorithms for VR training in gynaecologic laparoscopy.
A detailed finite element model of the human kidney for trauma research has been created directly from the National Library of Medicine Visible Human Female (VHF) Project data set. An image segmentation and organ reconstruction software package has been developed and employed to transform the 2D VHF images into a 3D polygonal representation. Nonuniform rational B-spline (NURBS) surfaces were then mapped to the polygonal surfaces, and were finally utilized to create a robust 3D hexahedral finite element mesh within a commercially available meshing software. The model employs a combined viscoelastic and hyperelastic material model to successfully simulate the behaviour of biological soft tissues. The finite element model was then validated for use in biomechanical research.
We report on a virtual anatomical preparation of the abdomen and pelvis of the Visible Human Female (VHF) for laparoscopic surgery training. The detailed cross-sectional image data set from the U.S. National Library of Medicine was used as the basis to build an exemplary model of the female abdomen and pelvis. Segmentation software was developed to delineate organ outlines and more than 300 structures of interest, including organs, blood vessels, bones, muscles, and ligaments, have been segmented and three-dimensionally reconstructed. Analyzing the normal anatomy we found several variations and pathologies of the VHF, such as missing muscles (gemellus superior, psoas minor), additional veins as well as spondylophytes (vertebral column, pubic bone), and colon diverticula. The complete data set may be viewed on the home page of the project (http://www.vision.ee.ethz.ch/projects/Lasso/start.html).
Abstract. Semi-automatic segmentation approaches tend to overlook the problems caused by missing or incomplete image information. In such situations, powerful control mechanisms and intuitive modelling metaphors should be provided in order to make the methods practically applicable. Taking this problem into account, the usage of subdivision curves in combination with the simulation of edge attracted mass points is proposed as a novel way towards a more robust interactive segmentation methodology. Subdivision curves provide a hierarchical and smooth representation of a shape which can be modified on coarse and on fine scales as well. Furthermore, local adaptive subdivision gives the required flexibility when dealing with a discrete curve representation. In order to incorporate image information, the control vertices of a curve are considered mass points, attracted by edges in the local neighbourhood of the image. This so-called Tamed Snake framework is illustrated by means of the segmentation of two medical data sets and the results are compared with those achieved by traditional Snakes.
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