Kainmueller Dagmar, Lamecker Hans, Zachow Stefan, Hege Hans-Christian
Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6345-51. doi: 10.1109/IEMBS.2009.5333269.
In this paper we propose a framework for fully automatic, robust and accurate segmentation of the human pelvis and proximal femur in CT data. We propose a composite statistical shape model of femur and pelvis with a flexible hip joint, for which we extend the common definition of statistical shape models as well as the common strategy for their adaptation. We do not analyze the joint flexibility statistically, but model it explicitly by rotational parameters describing the bent in a ball-and-socket joint. A leave-one-out evaluation on 50 CT volumes shows that image driven adaptation of our composite shape model robustly produces accurate segmentations of both proximal femur and pelvis. As a second contribution, we evaluate a fine grain multi-object segmentation method based on graph optimization. It relies on accurate initializations of femur and pelvis, which our composite shape model can generate. Simultaneous optimization of both femur and pelvis yields more accurate results than separate optimizations of each structure. Shape model adaptation and graph based optimization are embedded in a fully automatic framework.
在本文中，我们提出了一个用于在CT数据中对人体骨盆和股骨近端进行全自动、稳健且准确分割的框架。我们提出了一种具有灵活髋关节的股骨和骨盆复合统计形状模型，为此我们扩展了统计形状模型的通用定义及其适配的通用策略。我们没有对关节灵活性进行统计分析，而是通过描述球窝关节弯曲的旋转参数对其进行显式建模。对50个CT体积数据进行的留一法评估表明，我们的复合形状模型的图像驱动适配能够稳健地生成股骨近端和骨盆的准确分割。作为第二项贡献，我们评估了一种基于图优化的细粒度多目标分割方法。它依赖于我们的复合形状模型能够生成的股骨和骨盆的准确初始化。对股骨和骨盆同时进行优化比分别对每个结构进行优化能产生更准确的结果。形状模型适配和基于图的优化被嵌入到一个全自动框架中。
