Name
Abstract | ||
|---|---|---|
The generation of variable surgical scenes is a key element for effective training with surgery simulators. Our current research aims at a high fidelity hysteroscopy simulator which challenges the trainee with a new surgical scene in every training session. We previously reported on methods able to generate a broad range of pathologies within an existing healthy organ model. This paper presents the methods necessary to produce variable models of the healthy organ. In order to build a database of uteri, a volunteer study was conducted. The segmentation was carried out interactively, also covering the establishment of an anatomically meaningful correspondence between the individual organs. The variability of the shape parameters has been characterized by principal component analysis. A new method has been developed and tested, allowing the derivation of realistic new instances based on the stochastic model and complying with non-linear shape constraints which are defined and interactively controlled by medical experts. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.media.2005.11.003 | Medical Image Analysis |
Keywords | Field | DocType |
Statistical shape analysis,Surgical simulator,Anatomy,Uterus | High fidelity,Computer vision,Statistical shape analysis,Simulation,Segmentation,Artificial intelligence,Stochastic modelling,Computer graphics,Surgical training,Machine learning,Mathematics,Data display | Journal |
Volume | Issue | ISSN |
10 | 2 | 1361-8415 |
Citations | PageRank | References |
13 | 0.86 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R Sierra | 1 | 13 | 0.86 |
| G Zsemlye | 2 | 13 | 0.86 |
| Gábor Székely | 3 | 1697 | 193.47 |
| M. Bajka | 4 | 58 | 9.12 |