Hernandez Monica, Frangi Alejandro F
Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain.
Med Image Anal. 2007 Jun;11(3):224-41. doi: 10.1016/j.media.2007.01.002. Epub 2007 Feb 25.
Segmentation of vascular structures is a difficult and challenging task. In this article, we present an algorithm devised for the segmentation of such structures. Our technique consists in a geometric deformable model with associated energy functional that incorporates high-order multiscale features in a non-parametric statistical framework. Although the proposed segmentation method is generic, it has been applied to the segmentation of cerebral aneurysms in 3DRA and CTA. An evaluation study over 10 clinical datasets indicate that the segmentations obtained by our method present a high overlap index with respect to the ground-truth (91.13% and 73.31%, respectively) and that the mean error distance from the surface to the ground truth is close to the in-plane resolution (0.40 and 0.38 mm, respectively). Besides, our technique favorably compares to other alternative techniques based on deformable models, namely parametric geodesic active regions and active contours without edges.
血管结构的分割是一项困难且具有挑战性的任务。在本文中,我们提出了一种专为分割此类结构而设计的算法。我们的技术包括一个带有相关能量泛函的几何可变形模型,该模型在非参数统计框架中纳入了高阶多尺度特征。尽管所提出的分割方法具有通用性,但已应用于3DRA和CTA中脑动脉瘤的分割。对10个临床数据集的评估研究表明,我们的方法获得的分割结果与真实情况具有较高的重叠指数(分别为91.13%和73.31%),并且从表面到真实情况的平均误差距离接近平面分辨率(分别为0.40和0.38毫米)。此外,我们的技术与基于可变形模型的其他替代技术相比具有优势,即参数化测地线活动区域和无边缘活动轮廓。