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dc.contributor.authorGillmann, Christinaen_US
dc.contributor.authorSaur, Dorotheeen_US
dc.contributor.authorWischgoll, Thomasen_US
dc.contributor.authorScheuermann, Geriken_US
dc.contributor.editorSmit, Noeska and Vrotsou, Katerina and Wang, Beien_US
dc.date.accessioned2021-06-12T11:12:44Z
dc.date.available2021-06-12T11:12:44Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14333
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14333
dc.description.abstractMedical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty-aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty-aware medical imaging.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectMedical Visualization
dc.subjectUncertainty Visualization
dc.subjectSurvey
dc.titleUncertainty-aware Visualization in Medical Imaging - A Surveyen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersFrom Music to Medical Imaging
dc.description.volume40
dc.description.number3
dc.identifier.doi10.1111/cgf.14333
dc.identifier.pages665-689
dc.description.documenttypestar


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