Clark Kristi A, Woods Roger P, Rottenberg David A, Toga Arthur W, Mazziotta John C
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 660 Charles E. Young Drive South, Los Angeles, CA 90095, USA.
Neuroimage. 2006 Jan 1;29(1):185-202. doi: 10.1016/j.neuroimage.2005.07.035. Epub 2005 Aug 31.
The segmentation of T1-weighted images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is a fundamental processing step in neuroimaging, the results of which affect many other structural imaging analyses. Variability in the segmentation process can decrease the power of a study to detect anatomical differences, and minimizing such variability can lead to more robust results. This paper outlines a straightforward strategy that can be used (1) to select more optimal data acquisition and processing protocols and (2) to quantify the impact of such optimization. Using this approach with multiple scans of a single subject, we found that the choice of a segmentation algorithm had the largest impact on variability, while the choice of a pulse sequence had the second largest impact. The data indicate that the classification of GM is the most variable, and that the optimal protocol may differ across tissue types. Therefore, the intended use of segmentation data should play a role in optimization. Examples are provided to demonstrate that the minimization of variability is not sufficient for optimization; the overall accuracy of the approach must also be considered. Simple volumetric computations are included to illustrate the potential gain of optimization; these results show that volume estimates from optimal pathways were on average three times less variable than estimates from suboptimal pathways. Therefore, the simple strategy illustrated here can be applied to many studies to optimize tissue segmentation, which should lead to a net increase in the power of structural neuroimaging studies.
将T1加权图像分割为灰质（GM）、白质（WM）和脑脊液（CSF）是神经影像学中的一个基本处理步骤，其结果会影响许多其他结构成像分析。分割过程中的变异性会降低研究检测解剖差异的能力，而将这种变异性降至最低可带来更可靠的结果。本文概述了一种简单的策略，可用于（1）选择更优的数据采集和处理方案，以及（2）量化这种优化的影响。对同一受试者进行多次扫描并使用这种方法，我们发现分割算法的选择对变异性影响最大，而脉冲序列的选择影响次之。数据表明，灰质的分类变异性最大，并且最优方案可能因组织类型而异。因此，分割数据的预期用途应在优化中发挥作用。文中提供了示例以证明变异性最小化不足以实现优化；还必须考虑该方法的整体准确性。文中包含简单的体积计算以说明优化的潜在收益；这些结果表明，来自最优路径的体积估计值的变异性平均比来自次优路径的估计值小三倍。因此，这里阐述的简单策略可应用于许多研究以优化组织分割，这将使结构神经影像学研究的效能得到净提升。
