Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Twin Cities, USA.
School of Mathematics, University of Minnesota, Twin Cities, USA.
Med Image Anal. 2018 Jul;47:95-110. doi: 10.1016/j.media.2018.04.003. Epub 2018 Apr 17.
We present a two-stage variational approach for segmenting 3D bone CT data that performs robustly with respect to thin cartilage interfaces. In the first stage, we minimize a flux-augmented Chan-Vese model that accurately segments well-separated regions. In the second stage, we apply a new phase-field fracture inspired model that reliably eliminates spurious bridges across thin cartilage interfaces, resulting in an accurate segmentation topology, from which each bone object can be identified. Its mathematical formulation is based on the phase-field approach to variational fracture, which naturally blends with the variational approach to segmentation. We successfully test and validate our methodology for the segmentation of 3D femur and vertebra bones, which feature thin cartilage regions in the hip joint, the intervertebral disks, and synovial joints of the spinous processes. The major strength of the new methodology is its potential for full automation and seamless integration with downstream predictive bone simulation in a common finite element framework.
我们提出了一种两阶段的变分方法，用于分割 3D 骨骼 CT 数据，该方法在处理薄软骨界面时表现稳健。在第一阶段，我们最小化通量增强的 Chan-Vese 模型，该模型可以准确地分割分隔良好的区域。在第二阶段，我们应用一种新的相位场断裂启发模型，该模型可以可靠地消除薄软骨界面上的虚假桥接，从而得到准确的分割拓扑结构，从中可以识别每个骨骼对象。它的数学公式基于变分断裂的相位场方法，该方法自然与分割的变分方法融合在一起。我们成功地测试和验证了我们的方法，用于分割 3D 股骨和椎骨，这些骨骼在髋关节、椎间盘和棘突的滑膜关节中具有薄的软骨区域。新方法的主要优点是它具有完全自动化的潜力，并可以与下游的骨骼模拟无缝集成到通用的有限元框架中。
