Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.
J Magn Reson Imaging. 2012 Jan;35(1):156-65. doi: 10.1002/jmri.22809. Epub 2011 Oct 26.
To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery.
A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data. Image data of 45 subjects was used to validate the method by comparing manual and automatic segmentations. Vessel wall thickness and volume measurements obtained by both methods were compared.
Substantial agreement was observed between manual and automatic segmentation; over 85% of the vessel wall contours were segmented successfully. The interclass correlation was 0.690 for the vessel wall thickness and 0.793 for the vessel wall volume. Compared with manual image analysis, the automated method demonstrated improved interobserver agreement and inter-scan reproducibility. Additionally, the proposed automated image analysis approach was substantially faster.
This new automated method can reduce analysis time and enhance reproducibility of the quantification of vessel wall dimensions in clinical studies.
开发并验证一种自动分割技术，用于检测颈总动脉磁共振血管壁成像研究中的管腔和外壁边界。
新的分割方法采用三维（3D）可变形血管模型，仅需一次用户交互，结合 3D 磁共振血管造影（MRA）和 2D 血管壁图像。该血管模型是一个 3D 圆柱非均匀有理 B 样条（NURBS）曲面，可以变形以拟合基础图像数据。使用 45 名受试者的图像数据来验证该方法，通过比较手动和自动分割。比较两种方法获得的血管壁厚度和体积测量值。
手动和自动分割之间观察到高度一致；超过 85%的血管壁轮廓成功分割。血管壁厚度的组内相关系数为 0.690，血管壁体积为 0.793。与手动图像分析相比，自动化方法显示出更好的观察者间一致性和扫描间可重复性。此外，所提出的自动图像分析方法速度大大提高。
这种新的自动方法可以减少分析时间，提高临床研究中血管壁尺寸定量的可重复性。
