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dc.contributor.authorMartinke, Hannesen_US
dc.contributor.authorPetry, Christianen_US
dc.contributor.authorGroßkopf, Stefanen_US
dc.contributor.authorSuehling, Michaelen_US
dc.contributor.authorSoza, Grzegorzen_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.authorMistelbauer, Gabrielen_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:43Z
dc.date.available2017-09-06T07:12:43Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.urihttp://dx.doi.org/10.2312/vcbm.20171249
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171249
dc.description.abstractThe assessment of rib bone fractures and lesions consists of many images that have to be thoroughly inspected slice-by-slice and rib-by-rib. Existing visualization methods, such as curved planar reformation (CPR), reduce the number of images to inspect and, in turn, the time spent per case. However, this task remains time-consuming and exhausting. In this paper, we propose a novel rib unfolding strategy that considers the cross-sectional shape of each rib individually and independently. This leads to shape-adaptive slices through the ribs. By aggregating these slices into a single image, we support radiologists with a concise overview visualization of the entire rib cage for fracture and lesion assessment. We present results of our approach along different cases of rib and spinal fractures as well as lesions. To assess the applicability of our method, we separately evaluated the segmentation (with 954 data sets) and the visualization (with two clinical coaches).en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts
dc.subjectHuman
dc.subjectcentered computing
dc.subjectScientific visualization
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.subjectImage processing
dc.subjectApplied computing
dc.subjectHealth informatics
dc.titleBone Fracture and Lesion Assessment using Shape-Adaptive Unfoldingen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.description.sectionheadersShape and Models
dc.identifier.doi10.2312/vcbm.20171249
dc.identifier.pages149-158


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