Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation

TLDR

Direct Volume Rendering is widely used clinically for medical data, yet it lacks information on classification uncertainty, a critical factor in diagnostic accuracy. This study proposes animation methods to convey uncertainty in volume rendering. The authors employ a probabilistic Transfer Function model that animates uncertainty by sampling the probability domain over time, allows user interaction, and includes a sensitivity lens to focus on uncertain regions. Radiologists evaluated the technique in a stenosis assessment task and found the animation outperformed traditional rendering in assessment accuracy.

Abstract

Direct Volume Rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.