Center for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra (UPF), Barcelona, Spain.
Med Image Anal. 2012 May;16(4):889-903. doi: 10.1016/j.media.2012.01.006. Epub 2012 Feb 8.
The geometry of the carotid siphon has a large variability between subjects, which has prompted its study as a potential geometric risk factor for the onset of vascular pathologies on and off the internal carotid artery (ICA). In this work, we present a methodology for an objective and extensive geometric characterization of carotid siphon parameterized by a set of anatomical landmarks. We introduce a complete and automated characterization pipeline. Starting from the segmentation of vasculature from angiographic image and its centerline extraction, we first identify ICA by characterizing vessel tree bifurcations and training a support vector machine classifier to detect ICA terminal bifurcation. On ICA centerline curve, we detect anatomical landmarks of carotid siphon by modeling it as a sequence of four bends and selecting their centers and interfaces between them. Bends are detected from the trajectory of the curvature vector expressed in the parallel transport frame of the curve. Finally, using the detected landmarks, we characterize the geometry in two complementary ways. First, with a set of local and global geometric features, known to affect hemodynamics. Second, using large deformation diffeomorphic metric curve mapping (LDDMCM) to quantify pairwise shape similarity. We processed 96 images acquired with 3D rotational angiography. ICA identification had a cross-validation success rate of 99%. Automated landmarking was validated by computing limits of agreement with the reference taken to be the locations of the manually placed landmarks averaged across multiple observers. For all but one landmark, either the bias was not statistically significant or the variability was within 50% of the inter-observer one. The subsequently computed values of geometric features and LDDMCM were commensurate to the ones obtained with manual landmarking. The characterization based on pair-wise LDDMCM proved better in classifying the carotid siphon shape classes than the one based on geometric features. The proposed characterization provides a rich description of geometry and is ready to be applied in the search for geometric risk factors of the carotid siphon.
颈动脉虹吸的几何形状在不同个体之间存在很大的可变性，这促使人们将其作为颈内动脉（ICA）内外血管病变潜在的几何风险因素进行研究。在这项工作中，我们提出了一种使用一组解剖学标记对颈动脉虹吸进行客观和广泛的几何特征描述的方法。我们引入了一个完整和自动化的特征描述管道。从血管造影图像的分割及其中心线提取开始，我们首先通过特征化血管树分支并训练支持向量机分类器来检测 ICA 末端分支来识别 ICA。在 ICA 中心线曲线上，我们通过将其建模为四个弯曲的序列并选择它们的中心及其之间的界面来检测颈动脉虹吸的解剖学标记。弯曲是从在曲线的平行传输框架中表示的曲率向量的轨迹中检测到的。最后，使用检测到的标记，我们以两种互补的方式来描述几何形状。首先，使用一组已知影响血液动力学的局部和全局几何特征。其次，使用大变形仿射度量曲线映射（LDDMCM）来量化成对形状相似性。我们处理了 96 张使用 3D 旋转血管造影术获得的图像。ICA 识别的交叉验证成功率为 99%。自动标记的验证是通过计算与参考值的一致性限来完成的，参考值是由多个观察者平均手动放置标记的位置。除了一个标记外，要么偏差不具有统计学意义，要么变异性在 50%以内观察者之间的变异性。随后计算的几何特征和 LDDMCM 值与手动标记获得的值相当。基于成对 LDDMCM 的分类比基于几何特征的分类更能区分颈动脉虹吸的形状类别。所提出的特征描述提供了对几何形状的丰富描述，并且准备好应用于颈动脉虹吸的几何风险因素的研究。
