Extracting and Visualizing Uncertainties in Segmentations from 3D Medical Data

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dc.contributor.authorFaltin, Peteren_US
dc.contributor.authorChaisaowong, Kraisornen_US
dc.contributor.authorKraus, Thomasen_US
dc.contributor.authorMerhof, Doriten_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.abstractAssessing surfaces of segmentations extracted from 3D image data for medical purposes requires dedicated extraction and visualization methods. In particular, when assessing follow-up cases, the exact volume and confidence level of the segmentation surface is crucial for medical decision-making. This paper introduces a new processing chain comprising a series of carefully selected and well-matched steps to determine and visualize a segmentation boundary. In a first step, the surface, segmentation confidence and statistical partial volume are extracted. Then, a mesh-based method is applied to determine a refined boundary of the segmented object based on these properties, whilst smoothness, confidence of the surface and partial volume are considered locally. In contrast to existing methods, the proposed approach is able to guarantee the estimated volume for the whole segmentation, which is an important prerequisite for clinical application. Furthermore, a novel visualization method is presented which was specifically designed to simultaneously provide information about 3D morphology, confidence and possible errors. As opposed to classical visualization approaches that take advantage of color and transparency but need some geometric mapping and interpretation from the observer, the proposed scattered visualization utilizes density and scattering, which are much closer and more intuitively related to the original geometric meaning. The presented method is particularly suitable to assess pleural thickenings from follow-up CT images, which further illustrates the potential of the proposed method.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.20141181en_US
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vcbm.20141181.031-039
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectBoundary representationsen_US
dc.subjecten_US
dc.subjectI.4.6 [Image Processing and Computer Vision]en_US
dc.subjectEdge and feature detectionen_US
dc.titleExtracting and Visualizing Uncertainties in Segmentations from 3D Medical Dataen_US
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VCBM 14: Eurographics Workshop on Visual Computing for Biology and Medicine