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Automatic Meshing of Femur Cortical Surfaces from Clinical CT Images

机译:通过临床CT图像自动对股骨皮质表面进行网格划分

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摘要

We present an automated image-to-mesh workflow that meshes the cortical surfaces of the human femur, from clinical CT images. A piecewise parametric mesh of the femoral surface is customized to the in-image femoral surface by an active shape model. Then, by using this mesh as a first approximation, we segment cortical surfaces via a model of cortical morphology and imaging characteristics. The mesh is then customized further to represent the segmented inner and outer cortical surfaces. We validate the accuracy of the resulting meshes against an established semi-automated method. Root mean square error for the inner and outer cortical meshes were 0.74 mm and 0.89 mm, respectively. Mean mesh thickness absolute error was 0.03 mm with a standard deviation of 0.60 mm. The proposed method produces meshes that are correspondent across subjects, making it suitable for automatic collection of cortical geometry for statistical shape analysis.
机译:我们提出了一种自动的图像到网格工作流程,该网格可以根据临床CT图像对人股骨的皮质表面进行网格划分。通过活动形状模型将股骨表面的分段参数网格定制为图像中股骨表面。然后,通过使用该网格作为一阶近似,我们通过皮质形态和成像特征模型对皮质表面进行分割。然后进一步定制网格以表示分段的皮质内表面和皮质外表面。我们根据已建立的半自动方法验证所得网格的准确性。内部和外部皮质网格的均方根误差分别为0.74 mm和0.89 mm。平均筛网厚度绝对误差为0.03 mm,标准偏差为0.60 mm。所提出的方法产生的网格在对象之间是相对应的,使其适合于自动收集皮质几何以进行统计形状分析。

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    Auckland Bioengineering Institute, The University of Auckland, New Zealand;

    Auckland Bioengineering Institute, The University of Auckland, New Zealand;

    Clinical Applications Research Center, Toshiba Medical, Sydney, Australia;

    The Melbourne Dental School, The University of Melbourne, Victoria, Australia;

    Auckland Bioengineering Institute, The University of Auckland, New Zealand,Department of Engineering Science, The University of Auckland, New Zealand;

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