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首页> 外文会议>Conference on Medical Imaging: Visualization, Image-Guided Procedures, and Modeling >Acetabular rim and surface segmentation for hip surgery planning and dysplasia evaluation
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Acetabular rim and surface segmentation for hip surgery planning and dysplasia evaluation

机译：髋关节手术规划和发育不良评估的髋臼轮辋和表面分割

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Knowledge of the acetabular rim and surface can be invaluable for hip surgery planning and dysplasia evaluation. The acetabular rim can also be used as a landmark for registration purposes. At the present time acetabular features are mostly extracted manually at great cost of time and human labor. Using a recent level set algorithm that can evolve on the surface of a 3D object represented by a triangular mesh we automatically extracted rims and surfaces of acetabulae. The level set is guided by curvature features on the mesh. It can segment portions of a surface that are bounded by a line of extremal curvature (ridgeline or crestline). The rim of the acetabulum is such an extremal curvature line. Our material consists of eight hemi-pelvis surfaces. The algorithm is initiated by putting a small circle (level set seed) at the center of the acetabular surface. Because this surface distinctively has the form of a cup we were able to use the Shape Index feature to automatically extract an approximate center. The circle then expands and deforms so as to take the shape of the acetabular rim. The results were visually inspected. Only minor errors were detected. The algorithm also proved to be robust. Seed placement was satisfactory for the eight hemi-pelvis surfaces without changing any parameters. For the level set evolution we were able to use a single set of parameters for seven out of eight surfaces.
机译：对髋臼缘和表面的知识对于髋关节手术规划和发育不良评估可能是无价的。髋臼轮辋也可用作登记目的的标志标准。在当前时间，髋臼特征大多以巨大的时间和人工劳动力手动提取。使用最近的级别集合算法可以在三角网格表示的3D对象的表面上发展，我们自动提取aceTabula的轮辋和表面。级别集通过网格上的曲率特征引导。它可以段的部分由极端曲率（脊线或Crestline）限定的表面。髋臼的边缘是如此的极值曲率线。我们的材料由八个半骨盆表面组成。通过将髋臼表面的中心放置小圆圈（电平设置种子）来启动该算法。因为这种表面明显地具有杯子的形式，我们能够使用形状索引特征来自动提取近似中心。然后圆圈膨胀和变形，以采用髋臼边缘的形状。目测检查结果。只检测到微小的错误。该算法也被证明是坚固的。八个半骨盆表面的种子放置令人满意而不改变任何参数。对于级别集的演变，我们能够使用八个曲面中的七组参数。

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