Zhuge Feng, Rubin Geoffrey D, Sun Shaohua, Napel Sandy
Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.
Med Phys. 2006 May;33(5):1440-53. doi: 10.1118/1.2193247.
We present a system for segmenting the human aortic aneurysm in CT angiograms (CTA), which, in turn, allows measurements of volume and morphological aspects useful for treatment planning. The system estimates a rough "initial surface," and then refines it using a level set segmentation scheme augmented with two external analyzers: The global region analyzer, which incorporates a priori knowledge of the intensity, volume, and shape of the aorta and other structures, and the local feature analyzer, which uses voxel location, intensity, and texture features to train and drive a support vector machine classifier. Each analyzer outputs a value that corresponds to the likelihood that a given voxel is part of the aneurysm, which is used during level set iteration to control the evolution of the surface. We tested our system using a database of 20 CTA scans of patients with aortic aneurysms. The mean and worst case values of volume overlap, volume error, mean distance error, and maximum distance error relative to human tracing were 95.3% +/- 1.4% (s.d.); worst case = 92.9%, 3.5% +/- 2.5% (s.d.); worst case = 7.0%, 0.6 +/- 0.2 mm (s.d.); worst case = 1.0 mm, and 5.2 +/- 2.3 mm (s.d.); worst case = 9.6 mm, respectively. When implemented on a 2.8 GHz Pentium IV personal computer, the mean time required for segmentation was 7.4 +/- 3.6 min (s.d.). We also performed experiments that suggest that our method is insensitive to parameter changes within 10% of their experimentally determined values. This preliminary study proves feasibility for an accurate, precise, and robust system for segmentation of the abdominal aneurysm from CTA data, and may be of benefit to patients with aortic aneurysms.
我们提出了一种用于在CT血管造影（CTA）中分割人体主动脉瘤的系统，该系统进而能够测量对于治疗规划有用的体积和形态学方面的数据。该系统先估计一个粗略的“初始表面”，然后使用一种水平集分割方案对其进行细化，该方案通过两个外部分析器进行增强：全局区域分析器，它整合了主动脉及其他结构的强度、体积和形状的先验知识；以及局部特征分析器，它利用体素位置、强度和纹理特征来训练和驱动支持向量机分类器。每个分析器输出一个值，该值对应于给定体素是动脉瘤一部分的可能性，在水平集迭代期间用于控制表面的演化。我们使用一个包含20例主动脉瘤患者CTA扫描的数据库对我们的系统进行了测试。相对于人工追踪的体积重叠、体积误差、平均距离误差和最大距离误差的均值和最坏情况值分别为95.3%±1.4%（标准差）；最坏情况=92.9%，3.5%±2.5%（标准差）；最坏情况=7.0%，0.6±0.2毫米（标准差）；最坏情况=1.0毫米，以及5.2±2.3毫米（标准差）；最坏情况=9.6毫米。在一台2.8 GHz奔腾IV个人计算机上实现时，分割所需的平均时间为7.4±3.6分钟（标准差）。我们还进行了实验，结果表明我们的方法对参数在其实验确定值的10%范围内的变化不敏感。这项初步研究证明了从CTA数据中准确、精确且稳健地分割腹主动脉瘤系统的可行性，并且可能对主动脉瘤患者有益。
