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dc.contributor.authorSchuchter, Arthur
dc.contributor.authorPetrarolo, Giovanni
dc.contributor.authorGrießner, Dominik
dc.date.accessioned2023-10-12T11:06:29Z
dc.date.available2023-10-12T11:06:29Z
dc.date.issued2022
dc.description.abstractOver the decades medical analyses have grown in importance and practice possibilities. Digital operations like split-image procedures have become an integral part of modern medical practices and are still gaining momentum. Through the application of this technique medical computed tomography (CT) and magnetic resonance imaging scans (MRI) can be converted to three-dimensional bodies. This paper intends to use these generated objects in a process called segmentation. During this procedure, the desired area gets separated from the rest of the body for analysis and calculation of its physical properties. Furthermore, segmentation can be greatly improved through the utilization of algorithms such as U-Net and K-means or features like augmented reality (AR) or virtual reality (VR). The goal of this paper is to provide doctors with a reliable and simple to use program, which greatly aides in the analysis of patients’ data.en_US
dc.identifier.citationA. Schuchter, G. Petrarolo and D. Grießner, "Real-Time Voxel-based 2D to 3D CT Visualisation Framework for Volumetric Capacity Estimation," 2022 E-Health and Bioengineering Conference (EHB), Iasi, Romania, 2022.en_US
dc.identifier.cristinIDFRIDAID 2115068
dc.identifier.doi10.1109/EHB55594.2022.9991516
dc.identifier.urihttps://hdl.handle.net/10037/31535
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleReal-Time Voxel-based 2D to 3D CT Visualisation Framework for Volumetric Capacity Estimationen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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