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2 Publications

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    Kainmueller Lab
    09/24/10 | Improving deformable surface meshes through omni-directional displacements and MRFs.
    Kainmueller D, Lamecker H, Seim H, Zachow S, Hege HC
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010;13(Pt 1):227-34

    Deformable surface models are often represented as triangular meshes in image segmentation applications. For a fast and easily regularized deformation onto the target object boundary, the vertices of the mesh are commonly moved along line segments (typically surface normals). However, in case of high mesh curvature, these lines may intersect with the target boundary at "non-corresponding" positions, or even not at all. Consequently, certain deformations cannot be achieved. We propose an approach that allows each vertex to move not only along a line segment, but within a surrounding sphere. We achieve globally regularized deformations via Markov Random Field optimization. We demonstrate the potential of our approach with experiments on synthetic data, as well as an evaluation on 2 x 106 coronoid processes of the mandible in Cone-Beam CTs, and 56 coccyxes (tailbones) in low-resolution CTs.

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    Kainmueller Lab
    09/24/10 | Model-based auto-segmentation of knee bones and cartilage in MRI data.
    Seim H, Kainmueller D, Lamecker H, Bindernagel M, Malinowski J, Zachow S
    Medical Image Analysis for the Clinic - A Grand Challenge, MICCAI 2010, the 13th International Conference on Medical Image Computing and Computer Assisted Intervention. 2010 Sep 24:

    We present a method for fully automatic segmentation of the bones and cartilages of the human knee from MRI data. Based on statistical shape models and graph-based optimization, first the femoral and tibial bone surfaces are reconstructed. Starting from the bone sur- faces the cartilages are segmented simultaneously with a multi object technique using prior knowledge on the variation of cartilage thickness. We validate our method on 40 clinical MRI datasets acquired before knee replacement. 

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