Exploring Uncertainty in Image Segmentation Ensembles

Translated title of the contribution: Exploring Uncertainty in Image Segmentation Ensembles

Bernhard Fröhler, Torsten Möller, Johannes Weissenböck, Hans-Christian Hege, Johann Kastner, Christoph Heinzl

Research output: Chapter in Book/Report/Conference proceedingsConference contribution


Finding the most accurate image segmentation involves analyzing results from different algorithms or parameterizations. In this work, we identify different types of uncertainty in this analysis that are represented by the results of probabilistic algorithms, by the local variability in the segmentation, and by the variability across the segmentation ensemble. We propose visualization techniques for the analysis of such types of uncertainties in segmentation ensembles. For a global analysis we provide overview visualizations in the image domain as well as in the label space. Our probability probing and scatter plot based techniques facilitate a local analysis. We evaluate our techniques using a case study on industrial computed tomography data.
Translated title of the contributionExploring Uncertainty in Image Segmentation Ensembles
Original languageGerman
Title of host publicationProceedings of the 20th EG/VGTC Conference on Visualization
Number of pages3
Publication statusPublished - 2018
EventEurographics Conference on Visualization 2018 (EuroVis 2018) - Brünn, Czech Republic
Duration: 4 Jun 20188 Jun 2018


ConferenceEurographics Conference on Visualization 2018 (EuroVis 2018)
Country/TerritoryCzech Republic
Internet address


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