Uncertainty in Medical Visualization: Modeling, Visual Encoding, and Interactive Analysis

Basic data for this project

Description

Visualization methods have become an integral part of clinical routine supporting medical diagnosis, medical treatment planning, and intraoperative assistance. The medical visualizations are created based on certain assumptions and typically do not make the medical experts aware of those assumptions which may result in potential deviations of the shown picture from the actual situation. Hence, the medical experts perceive and interpret the visualization as a true image, on which decisions are based, at least, in part. However, the medical visualization pipeline ranging from the actual imaging step over registration and segmentation tasks to the final rendering step include many potential sources of errors. To allow for a more educated decision making, the impact of those error probabilities need to be quantitatively captured and visually conveyed to the medical expert. This is the task of uncertainty visualization. Herein, we propose a rigorous modeling of the appearing uncertainties and the development of methods for visually encoding them. Controlled studies are to be conducted to validate and compare the proposed methods. Based on those studies, interactive visual analysis systems for uncertainty-aware decision making are proposed. The resulting visualization systems are employed within three exemplary medical applications, where visualization plays a crucial role.