Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging
Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging
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DOI：
10.1203/pdr.0b013e31815ed071
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发表时间：
2008-02-01
影响因子：
3.6
通讯作者：
Van der Grond, Jeroen
中科院分区：
文献类型：
--
作者：
Anbeek, Petronella;Vincken, Koen L.;Van der Grond, Jeroen
一个完全自动化的方法已经开发了四个不同的结构在新生儿大脑的分割：白色物质（WM），中央灰质（CEGM），皮质灰质（COGM），和脑脊液（CSF）。分割算法基于来自T2加权（T2-w）和反转恢复（IR）扫描的信息。该方法使用K近邻（KNN）分类技术与来自空间信息和体素强度的功能。生成每种组织类型的概率分割。通过在这些概率图上应用阈值，获得二进制分割。通过与金标准进行比较来评价这些最终分割。计算灵敏度、特异性和Dice相似性指数（ST），以定量验证结果。相对于金标准，达到了高灵敏度和特异性：所有组织类型的灵敏度>0.82，特异性>0.9。从二进制和概率分割计算组织体积。所有组织类型的概率分割体积准确地估计了金标准体积。KNN方法为新生儿脑分割提供了有价值的方法。概率结果为精确的体积测量提供了有用的工具。所描述的方法基于常规诊断磁共振成像（MRI），适合大规模人群研究。
A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (ST) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.