Heimann Tobias, Münzing Sascha, Meinzer Hans-Peter, Wolf Ivo
Div. Medical and Biological Informatics, German Cancer Research Center, 69120 Heidelberg, Germany.
Inf Process Med Imaging. 2007;20:1-12. doi: 10.1007/978-3-540-73273-0_1.
We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a statistical shape model (SSM) of the structure of interest. A global search with an evolutionary algorithm is employed to detect suitable initial parameters for the model, which are subsequently optimized by a local search similar to the Active Shape mechanism. After that, a deformable mesh with the same topology as the SSM is used for the final segmentation: While external forces strive to maximize the posterior probability of the mesh given the local appearance around the boundary, internal forces governed by tension and rigidity terms keep the shape similar to the underlying SSM. To prevent outliers and increase robustness, we determine the applied external forces by an algorithm for optimal surface detection with smoothness constraints. The approach is evaluated on 54 CT images of the liver and reaches an average surface distance of 1.6 +/- 0.5 mm in comparison to manual reference segmentations.
我们提出了一种用于容积图像分割的新方法，该方法特别适用于高度可变的软组织结构。该算法的核心是感兴趣结构的统计形状模型（SSM）。采用进化算法进行全局搜索以检测模型的合适初始参数，随后通过类似于主动形状机制的局部搜索对其进行优化。之后，使用与SSM具有相同拓扑结构的可变形网格进行最终分割：外力力求在给定边界周围局部外观的情况下最大化网格的后验概率，而由张力和刚度项控制的内力使形状与基础SSM相似。为了防止异常值并提高鲁棒性，我们通过一种具有平滑约束的最优表面检测算法来确定所施加的外力。该方法在54幅肝脏CT图像上进行了评估，与手动参考分割相比，平均表面距离达到1.6 +/- 0.5毫米。
