Uncertainty-aware Ensemble of Classifiers for Segmenting Brain MRI Data

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dc.contributor.authorAl-Taie, Ahmeden_US
dc.contributor.authorHahn, Horst K.en_US
dc.contributor.authorLinsen, Larsen_US
dc.contributor.editorIvan Viola and Katja Buehler and Timo Ropinskien_US
dc.date.accessioned2014-12-16T07:36:54Z
dc.date.available2014-12-16T07:36:54Z
dc.date.issued2014en_US
dc.description.abstractEstimating and visualizing uncertainty in medical image segmentation has become an active research area due to the necessity of making medical experts aware of possibly wrong segmentation decisions. Still, to our knowledge all these methods are based on a single choice of the underlying segmentation approach. Segmentation using an ensemble of classifiers (or committee machine) use multiple classifiers to increase the performance when compared to applying a single classifier. In this paper, we propose methods to estimate uncertainties in segmentations produced by ensembles of classifiers. We investigate and compare the different combining strategies of the segmentation results of the ensemble members from an uncertainty point of view. We discuss why some combining strategies tend to perform better than others. Also, we visualize the estimated uncertainties using a color mapping in image space and propose a post-segmentations correction step to reclassify the noisy pixels in the final result based on the statistical uncertainty.en_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US
dc.identifier.isbn978-3-905674-62-0en_US
dc.identifier.issn2070-5778en_US
dc.identifier.urihttps://doi.org/10.2312/vcbm.20141182en_US
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vcbm.20141182.041-050
dc.publisherThe Eurographics Associationen_US
dc.titleUncertainty-aware Ensemble of Classifiers for Segmenting Brain MRI Dataen_US
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VCBM 14: Eurographics Workshop on Visual Computing for Biology and Medicine