Uncertainty and Reproducibility in Medical Visualization

Abstract

The medical visualization pipeline is affected by various sources of uncertainty. Many errors may occur and several assumptions are made in the various processing steps from the image acquisition to the rendering of the visualization output, which induce uncertainty. High uncertainty leads to low robustness of the algorithms impacting reproducibility of the results. We present how uncertainty can be mathematically described in the medical context. Moreover, in medical applications, the visualization is typically based on a segmentation of the medical images. We propose a method to capture uncertainty in image segmentation and present extensions to ensemble and multi-modal image segmentation.