Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA.
Comput Methods Programs Biomed. 2010 Jan;97(1):62-77. doi: 10.1016/j.cmpb.2009.07.009. Epub 2009 Aug 26.
With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention.
随着医学成像扫描仪的进步,生成大型多维数据集已经变得很常见。这些数据集需要快速、彻底分析的工具。为了满足这一需求,我们开发了一种自动形态分析算法,该算法结合了 A Visualization Workshop 的计算和图像处理库,用于三维分割、血管树生成和结构层次排序,并采用两级数值优化程序来估计血管直径。我们将这项新技术与我们的肺血管形态学数学模型相结合,以量化肺动脉树的结构和功能属性。我们的生理学研究需要对血管结构进行重复测量,以确定肺部疾病动物模型之间血管生物力学特性的差异。自动化具有许多优势,包括显著提高速度和最小化操作员交互和偏差。通过与之前发表的大鼠肺动脉微 CT 数据分析技术进行比较,对结果进行了验证,其中血管是使用强烈的操作员干预手动映射和测量的。