Exploring Uncertainty in Image Segmentation Ensembles
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
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.
Description
@inproceedings{10.2312:eurp.20181123,
booktitle = {EuroVis 2018 - Posters},
editor = {Anna Puig and Renata Raidou},
title = {{Exploring Uncertainty in Image Segmentation Ensembles}},
author = {Fröhler, Bernhard and Möller, Torsten and Weissenböck, Johannes and Hege, Hans-Christian and Kastner, Johann and Heinzl, Christoph},
year = {2018},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-065-9},
DOI = {10.2312/eurp.20181123}
}