Biomedical Engineering Graduate Program and Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 3K7, Canada.
Med Phys. 2011 May;38(5):2479-93. doi: 10.1118/1.3574887.
Three-dimensional ultrasound (3D US) of the carotid artery provides measurements of arterial wall and plaque [vessel wall volume (VWV)] that are complementary to the one-dimensional measurement of the carotid artery intima-media thickness. 3D US VWV requires an observer to delineate the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) of the carotid artery. The main purpose of this work was to develop and evaluate a semiautomated segmentation algorithm for delineating the MAB and LIB of the carotid artery from 3D US images.
To segment the MAB and LIB, the authors used a level set method and combined several low-level image cues with high-level domain knowledge and limited user interaction. First, the operator initialized the algorithm by choosing anchor points on the boundaries, identified in the images. The MAB was segmented using local region- and edge-based energies and an energy that encourages the boundary to pass through anchor points from the preprocessed images. For the LIB segmentation, the authors used local and global region-based energies, the anchor point-based energy, as well as a constraint promoting a boundary separation between the MAB and LIB. The data set consisted of 231 2D images (11 2D images per each of 21 subjects) extracted from 3D US images. The image slices were segmented five times each by a single observer using the algorithm and the manual method. Volume-based, region-based, and boundary distance-based metrics were used to evaluate accuracy. Moreover, repeated measures analysis was used to evaluate precision.
The algorithm yielded an absolute VWV difference of 5.0% +/- 4.3% with a segmentation bias of -0.9% +/- 6.6%. For the MAB and LIB segmentations, the method gave absolute volume differences of 2.5% +/- 1.8% and 5.6% +/- 3.0%, Dice coefficients of 95.4% +/- 1.6% and 93.1% +/- 3.1%, mean absolute distances of 0.2 +/- 0.1 and 0.2 +/- 0.1 mm, and maximum absolute distances of 0.6 +/- 0.3 and 0.7 +/- 0.6 mm, respectively. The coefficients of variation of the algorithm (5.1%) and manual methods (3.9%) were not significantly different, but the average time saved using the algorithm (2.8 min versus 8.3 min) was substantial.
The authors generated and tested a semiautomated carotid artery VWV measurement tool to provide measurements with reduced operator time and interaction, with high Dice coefficients, and with necessary required precision.
颈动脉的三维超声（3D US）提供了动脉壁和斑块[血管壁体积（VWV）]的测量，这些测量与颈动脉内膜中层厚度的一维测量相辅相成。3D US VWV 需要观察者描绘颈动脉的中膜-外膜边界（MAB）和管腔-内膜边界（LIB）。这项工作的主要目的是开发和评估一种半自动分割算法，用于从 3D US 图像中分割颈动脉的 MAB 和 LIB。
为了分割 MAB 和 LIB，作者使用水平集方法，并结合了几种低水平的图像线索以及高水平的领域知识和有限的用户交互。首先，操作者通过选择在图像中识别的边界上的锚点来初始化算法。MAB 的分割使用基于局部区域和边缘的能量以及一种能量，该能量鼓励边界通过预处理图像中的锚点穿过。对于 LIB 的分割，作者使用了基于局部和全局区域的能量、基于锚点的能量以及促进 MAB 和 LIB 之间边界分离的约束。数据集由 231 个二维图像（21 个受试者中每个受试者各 11 个二维图像）组成，这些图像是从 3D US 图像中提取出来的。由单个观察者使用算法和手动方法对图像切片进行了五次分割。使用基于体积、基于区域和基于边界距离的指标来评估准确性。此外，还使用重复测量分析来评估精度。
该算法的绝对 VWV 差异为 5.0% +/- 4.3%，分割偏差为-0.9% +/- 6.6%。对于 MAB 和 LIB 的分割，该方法的绝对体积差异分别为 2.5% +/- 1.8%和 5.6% +/- 3.0%，Dice 系数分别为 95.4% +/- 1.6%和 93.1% +/- 3.1%，平均绝对距离分别为 0.2 +/- 0.1 和 0.2 +/- 0.1 毫米，最大绝对距离分别为 0.6 +/- 0.3 和 0.7 +/- 0.6 毫米。算法（5.1%）和手动方法（3.9%）的变异系数没有显著差异，但使用算法（2.8 分钟对 8.3 分钟）可显著节省平均时间。
作者生成并测试了一种半自动颈动脉 VWV 测量工具，该工具可提供具有减少操作员时间和交互的测量值，具有较高的 Dice 系数，并且具有必要的精度。
