State Key Laboratory of Robotics and System, Harbin Institute of Technology, Nangang District, Harbin, Heilongjiang, China.
Acad Radiol. 2010 Jan;17(1):67-74. doi: 10.1016/j.acra.2009.07.013. Epub 2009 Sep 5.
The reference system based on the fourth ventricular landmarks (including the fastigial point and ventricular floor plane) is used in medical image analysis of the brain stem. The objective of this study was to develop a rapid, robust, and accurate method for the automatic identification of this reference system on T1-weighted magnetic resonance images.
The fully automated method developed in this study consisted of four stages: preprocessing of the data set, expectation-maximization algorithm-based extraction of the fourth ventricle in the region of interest, a coarse-to-fine strategy for identifying the fastigial point, and localization of the base point. The method was evaluated on 27 Brain Web data sets qualitatively and 18 Internet Brain Segmentation Repository data sets and 30 clinical scans quantitatively.
The results of qualitative evaluation indicated that the method was robust to rotation, landmark variation, noise, and inhomogeneity. The results of quantitative evaluation indicated that the method was able to identify the reference system with an accuracy of 0.7 +/- 0.2 mm for the fastigial point and 1.1 +/- 0.3 mm for the base point. It took <6 seconds for the method to identify the related landmarks on a personal computer with an Intel Core 2 6300 processor and 2 GB of random-access memory.
The proposed method for the automatic identification of the reference system based on the fourth ventricular landmarks was shown to be rapid, robust, and accurate. The method has potentially utility in image registration and computer-aided surgery.
基于第四脑室标志(包括 fastigial 点和脑室底面)的参考系统用于脑干的脑磁共振图像分析。本研究的目的是开发一种快速、稳健、准确的方法,用于自动识别 T1 加权磁共振图像上的参考系统。
本研究中开发的全自动方法包括四个阶段:数据集的预处理、感兴趣区域中基于期望最大化算法的第四脑室提取、粗到精的 fastigial 点识别策略和基点定位。该方法在 27 个 Brain Web 数据集上进行了定性评估,在 18 个 Internet Brain Segmentation Repository 数据集和 30 个临床扫描上进行了定量评估。
定性评估结果表明,该方法对旋转、标志变化、噪声和非均匀性具有鲁棒性。定量评估结果表明,该方法能够以 0.7 +/- 0.2 毫米的精度识别 fastigial 点,以 1.1 +/- 0.3 毫米的精度识别基点。该方法在具有 Intel Core 2 6300 处理器和 2GB 随机存取存储器的个人计算机上识别相关标志所需的时间不到 6 秒。
提出的基于第四脑室标志的参考系统自动识别方法快速、稳健、准确。该方法在图像配准和计算机辅助手术中具有潜在的应用价值。