Automatic 3-D model-based neuroanatomical segmentation
Automatic 3-D model-based neuroanatomical segmentation
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DOI：
10.1002/hbm.460030304
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发表时间：
1995-01-01
影响因子：
4.8
通讯作者：
Evans, AC
中科院分区：
文献类型：
--
作者：
Collins, DL;Holmes, CJ;Evans, AC
许多形式的定量神经解剖学分析都需要进行精确的分割。然而，手动方法耗时，并且在准确性和可重复性（精度）方面都容易出错。本文提出了一种基于三维模型的分割方法，用于根据人脑在磁共振图像（MRI）中的外观，全自动识别和描绘其大体解剖结构。该方法依赖于一种通用的、迭代的、分层的非线性配准程序以及一个包含基于体素强度的数据和几何图谱的人脑解剖三维数字模型。在这里，传统的分割策略被颠倒了：不是将理想化图谱中的几何轮廓直接与MRI数据匹配，而是通过识别在模型图像和新的MRI脑体积之间能最好地映射基于强度的对应特征的非线性空间变换来实现分割。完成后，在模型图像上定义的图谱轮廓通过相同的变换进行映射，以对新数据集中的各个结构进行分割和标记。使用手动分割的结构边界进行比较，对于真实的脑模体数据，体积差异和体积重叠的测量值分别小于2%和优于97%，对于人类MRI数据，分别小于10%和优于85%。这与观察者内变异性估计值（分别为4.9%和87%）相比具有优势。该程序性能良好，客观且其实现稳健。该程序不需要人工干预，因此适用于大量受试者的研究。如果目标体积已经在立体定向空间中，非线性图像匹配的通用方法对于将脑数据集非线性映射到立体定向空间也很有用。（C）1995威利 - 利斯公司
Explicit segmentation is required for many forms of quantitative neuroanatomic analysis. However, manual methods are time-consuming and subject to errors in both accuracy and reproducibility (precision). A 3-D model-based segmentation method is presented in this paper for the completely automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI).The approach depends on a general, iterative, hierarchical non-linear registration procedure and a 3-D digital model of human brain anatomy that contains both volumetric intensity-based data and a geometric atlas. Here, the traditional segmentation strategy is inverted: instead of matching geometric contours from an idealized atlas directly to the MRI data, segmentation is achieved by identifying the non-linear spatial transformation that best maps corresponding intensity-based features between a model image and a new MRI brain volume. When completed, atlas contours defined on the model image are mapped through the same transformation to segment and label individual structures in the new data set.Using manually segmented structure boundaries for comparison, measures of volumetric difference and volumetric overlap were less than 2% and better than 97% for realistic brain phantom data, and less than 10% and better than 85%, respectively, for human MRI data. This compares favorably to intra-observer variability estimates of 4.9% and 87%, respectively. The procedure performs well, is objective and its implementation robust. The procedure requires no manual intervention, and is thus applicable to studies of large numbers of subjects. The general method for non-linear image matching is also useful for non-linear mapping of brain data sets into stereotaxic space if the target volume is already in stereotaxic space. (C) 1995 Wiley-Liss, Inc.