Uncertainty in Medical Visualization: Bringing Imaging and Simulation Uncertainty Together

• Applicants: Professor Dr.-Ing. Lars Linsen; Professor Dr. Tobias Preusser
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2013 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 241370238

Visualization methods have become an integral part of clinical routine supporting diagnosis, 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 often perceive andinterpret the visualization as a true image, on which decisions are based, at least, in part. However, the medical visualization pipeline ranging from the actual image acquisition over registration and segmentation tasks to the final rendering includes 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 themedical expert. This is the task of uncertainty visualization. Within the first funding period, we developed a rigorous modeling of the appearing uncertainties and methods for visually encoding them, leading to interactive visual analysis systems for uncertainty-aware decision making. Beside using information derived from medical imaging data, decision making can further be improved by enhancing the system with patient-specific information from simulating biophysical or medical processes. Such simulations, on the other hand, are also based on the uncertain imaging data and even make further assumptions. Thus, their results also contain uncertainty potentially bearing clinical relevance. Our goal is to bring together imaging uncertainty and simulation uncertainty in a visual analysis system. Using the uncertainty-aware image segmentation of the first funding period, we propose to develop uncertainty-aware patient-specific simulations using probabilistic inputs, predictions of the simulation outcomes using deep-learning methods, and interactive visualizations for an uncertainty-aware analysis of patient-specific data from segmentations and simulations. The methods shall be employed within medical applications, where visualization plays a crucial role.

• DFG Programme: Research Grants