VCBM 12: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/464

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Browsing VCBM 12: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "and object representations"

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    Sketch-based Image-independent Editing of 3D Tumor Segmentations using Variational Interpolation
    (The Eurographics Association, 2012) Heckel, Frank; Braunewell, Stefan; Soza, Grzegorz; Tietjen, Christian; Hahn, Horst K.; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
    In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation correction tools, a problem which has not received much attention in research. Cases to be manually corrected are often particularly difficult and the image does often not provide enough information for segmentation, so we present an image-independent method for intuitive sketch-based editing of 3D tumor segmentations. It is based on an object reconstruction using variational interpolation and can be used in any 3D modality, such as CT or MRI. We also discuss sketch-based editing in 2D as well as a hole-correction approach for variational interpolation. Our manual correction algorithm has been evaluated on 89 segmentations of tumors in CT by 2 technical experts with 6+ years of experience in tumor segmentation and assessment. The experts rated the quality of our correction tool as acceptable or better in 92.1% of the cases. They needed a median number of 4 correction steps with one step taking 0.4s on average.
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    Visually Guided Mesh Smoothing for Medical Applications
    (The Eurographics Association, 2012) Moench, Tobias; Kubisch, Christoph; Lawonn, Kai; Westermann, Ruediger; Preim, Bernhard; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
    Surface models derived from medical image data often exhibit artifacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation, model quality is evaluated in real-time and provided to the user as quality graphs to support the mental optimization of input parameters. Moreover, this framework is used to find optimal smoothing parameters automatically and to provide data-specific parameter suggestions.