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Automatic identification of the reference system based on the fourth ventricular landmarks in T1-weighted MR images.

机译:基于T1加权MR图像中的第四个心室界标自动识别参考系统。

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RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.
机译:理由和目的:基于第四脑室标志物(包括小脑点和脑室底平面)的参考系统用于脑干的医学图像分析。这项研究的目的是开发一种快速,可靠且准确的方法,以在T1加权磁共振图像上自动识别该参考系统。材料与方法:本研究开发的全自动方法包括四个阶段:数据集的预处理,基于期望最大化算法的感兴趣区域中第四脑室的提取,从粗到精的识别策略。基准点和基点的定位。对该方法进行了定性的27个Brain Web数据集和18个Internet Brain Segmentation Repository数据集以及30个临床扫描的定量评估。结果:定性评估结果表明该方法对旋转,界标变化,噪声和不均匀性具有鲁棒性。定量评估的结果表明,该方法能够识别参考系统,其准确度为基点的精度为0.7 +/- 0.2 mm,基点的精度为1.1 +/- 0.3 mm。在用Intel Core 2 6300处理器和2 GB随机存取内存的个人计算机上识别相关地标的方法花费了不到6秒的时间。结论:提出的基于第四心室标志的参考系统自动识别方法被证明是快速,鲁棒和准确的。该方法在图像配准和计算机辅助手术中具有潜在的实用性。

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