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Automatic Meshing of Femur Cortical Surfaces from Clinical CT Images

机译:临床CT图像自动啮合股骨皮质表面

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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:我们介绍了一种自动图像到网格工作流,从临床CT图像内网格啮合人股骨的皮质表面。通过主动形状模型将股骨表面的分段参数啮合物定制到图像内股骨表面。然后,通过使用该网格作为第一近似,通过皮质形态和成像特性模型进行皮质表面。然后将网状物进一步定制以表示分段内和外皮质表面。我们针对建立的半自动方法验证所得网格的准确性。内皮网的根均方误差分别为0.74 mm和0.89mm。平均网格厚度绝对误差为0.03 mm,标准偏差为0.60 mm。所提出的方法产生跨对象的对应物的网格,使其适用于自动收集皮质几何形状以进行统计形状分析。

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