Automated landmarking and geometric characterization of the carotid siphon

详细信息	查看全文 | 推荐本文 |

• 作者：Hrvoje Bogunovi膰^a ; ^b ; ^{hrvoje.bogunovic@upf.edu} ; José ; Marí ; a Pozo^a ; ^b ; Rubé ; n Cá ; rdenes^a ; ^b ; Marí ; a Cruz Villa-Uriol^a ; ^b ; Raphaë ; l Blanc^c ; Michel Piotin^c ; Alejandro F. Frangi^a ; ^b ; ^d ; ^{alejandro.frangi@upf.edu}
• 关键词：Cerebral angiography ; Internal carotid artery ; Segmentation ; Geometric quantification ; Computational anatomy
• 刊名：Medical Image Analysis
• 出版年：2012
• 期刊代码：90_13618415
• 类别：cp
• 出版时间：May, 2012
• 卷：16
• 期：4
• 页码：889-903
• 文件大小：1960 K

摘要

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.