An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).
An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).
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DOI：
10.1016/j.mri.2017.02.002
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发表时间：
2017-06
影响因子：
2.5
通讯作者：
Schweser F
中科院分区：
文献类型：
--
作者：
Feng X;Deistung A;Dwyer MG;Hagemeier J;Polak P;Lebenberg J;Frouin F;Zivadinov R;Reichenbach JR;Schweser F
在许多神经成像应用中，需要准确和鲁棒的皮层下灰质（SGM）核的分割。FMRIB的集成配准和分割工具（FIRST）是基于T1加权（T1 w）图像进行自动皮层下分割的最受欢迎的软件工具之一。在这项工作中，我们证明，FIRST往往会产生不准确的SGM分割结果的情况下，异常的大脑解剖结构，如存在于萎缩的大脑，由于一个穷人的空间匹配的皮层下结构与训练数据在MNI空间，以及由于T1 w图像上的SGM结构的对比度不足。因此，这种与平均脑解剖结构的偏差可能会在临床研究中引入分析偏倚，这可能并不总是明显的，并且可能仍然无法识别。为了改善皮层下核的分割，我们建议使用FIRST结合一个特殊的混合图像对比度（HC）和非线性（NL）配准模块（HC-nlFIRST），其中混合图像对比度来自T1 w图像和磁化率图，以创建类似于蒙特利尔神经研究所（MNI）模板的皮层下对比度。在我们的方法中，非线性配准取代了FIRST的默认线性配准，使输入数据与MNI模板的对齐更加准确。我们评估了我们的方法对82例特别异常的脑解剖，从2000多个临床病例的数据库中选择。定性和定量分析表明，HC-nlFIRST提供了改进的分割相比，默认的FIRST方法。
Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T1-weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST’s default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of more than 2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method.