Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
Med Phys. 2013 May;40(5):051721. doi: 10.1118/1.4802751.
The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images.
The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion.
The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification.
With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.
狭窄程度是评估心血管疾病严重程度的重要生物标志物。我们的工作目的是开发和评估一种半自动方法,用于 CTA 图像中的颈动脉管腔分割和随后的颈动脉狭窄定量。
作者提出了一种半自动的狭窄检测和定量方法,用于管腔分割后。颈动脉管腔的分割分为三个步骤。首先,采用基于中值和强度与管腔相似性的度量的迭代最小成本路径方法提取颈内动脉和颈外动脉的中心线。其次,使用水平集方法描绘管腔边界,该方法由梯度信息、区域强度信息和空间信息引导。特别努力在基于局部中心线强度的附加项,以将所有可能的斑块组织从分割中排除。最后,通过对分割的最大内切球的包络施加形状约束来去除分割中的侧支。从分割的管腔中,根据北美症状性颈动脉内膜切除术试验标准检测并定量基于横截面积的和基于横截面积的狭窄程度。
该方法在 cls2009 挑战赛的公共数据库上进行了训练和测试。对于分割,作者获得了 90.2%的骰子相似系数和 0.34 毫米的平均绝对表面距离。对于狭窄定量,作者获得了基于横截面积的狭窄和基于横截面积的狭窄定量的平均误差分别为 15.7%和 19.2%。
根据这些结果,该方法在颈动脉管腔分割准确性方面排名第二,在颈动脉狭窄定量方面排名第一。