Optimum template selection for atlas-based segmentation
Optimum template selection for atlas-based segmentation
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DOI：
10.1016/j.neuroimage.2006.07.050
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发表时间：
2007-02-15
期刊：
影响因子：
5.7
通讯作者：
Aizenstein, Howard J.
中科院分区：
文献类型：
--
作者：
Wu, Minjie;Rosano, Caterina;Aizenstein, Howard J.
MR脑图像的基于图谱的分割通常使用单个图谱（例如，MNI Colin 27）进行区域识别。人类大脑结构的正常个体差异对图谱选择提出了重大挑战。以前的研究主要集中在如何为不同的需求创建特定的模板（例如，对于一定的人口）。我们用不同的方法来解决图谱选择问题：我们使用一系列大脑模板来进行基于图谱的分割，而不是选择固定的大脑图谱。对于每个受试者和每个区域，模板选择方法基于归一化互信息自动选择具有最高局部配准精度的“最佳”模板。通过人工追踪和自动标记结果之间的重叠率（OR）和类内相关系数（ICC）来量化模板选择方法和单模板方法的区域分类性能。两组大脑图像和多个感兴趣区域（ROI），包括右前扣带皮层（ACC）和几个皮层下结构，进行了测试。我们发现，在所有13个分析的ROI中，模板选择方法产生的OR显著高于单一模板方法（双侧配对t检验，右侧ACC在t（8）=4.353，p=0.0024;右侧杏仁核，配对t检验t（8）> 3.175，p < 0.013;对于其余ROI，t（8）= 4.36，p < 0.002）。模板选择方法也提供了更可靠的体积估计比单一模板的方法增加ICC。此外，使用最佳模板的基于图谱的分割的改进的准确性接近手动跟踪的准确性，因此对于自动化脑成像分析是有效的。(c)2006年爱思唯尔公司All rights reserved.
Atlas-based segmentation of MR brain images typically uses a single atlas (e.g., MNI Colin27) for region identification. Normal individual variations in human brain structures present a significant challenge for atlas selection. Previous researches mainly focused on how to create a specific template for different requirements (e.g., for a certain population). We address atlas selection with a different approach: instead of choosing a fixed brain atlas, we use a family of brain templates for atlas-based segmentation. For each subject and each region, the template selection method automatically chooses the 'best' template with the highest local registration accuracy, based on normalized mutual information. The region classification performances of the template selection method and the single template method were quantified by the overlap ratios (ORs) and intraclass correlation coefficients (ICCs) between the manual tracings and the respective automated labeled results. Two groups of brain images and multiple regions of interest (ROIs), including the right anterior cingulate cortex (ACC) and several subcortical structures, were tested for both methods. We found that the template selection metho produced significantly higher ORs than did the single template method across all of the 13 analyzed ROIs (two-tailed paired t-test, right ACC at t(8)=4.353, p=0.0024; right amygdala, matched paired t test t(8)> 3.175, p < 0.013; for the remaining ROIs, t(8) = 4.36, p < 0.002). The template selection method also provided more reliable volume estimates than the single template method with increased ICCs. Moreover, the improved accuracy of atlas-based segmentation using optimum templates approaches the accuracy of manual tracing, and thus is valid for automated brain imaging analyses. (c) 2006 Elsevier Inc. All rights reserved.