ProbExplorer: Uncertainty-guided Exploration and Editing of Probabilistic Medical Image Segmentation

Abstract

In this paper, we develop an interactive analysis and visualization tool for probabilistic segmentation results in medical imaging. We provide a systematic approach to analyze, interact and highlight regions of segmentation uncertainty. We introduce a set of visual analysis widgets integrating different approaches to analyze multivariate probabilistic field data with direct volume rendering. We demonstrate the user s ability to identify suspicious regions (e.g. tumors) and correct the misclassification results using a novel uncertainty-based segmentation editing technique. We evaluate our system and demonstrate its usefulness in the context of static and time-varying medical imaging datasets.