BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI
BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI
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DOI：
10.1016/j.patcog.2016.09.020
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Mohammad Khatami;T. Schmidt-Wilcke;P. Sundgren;Amin Abbasloo;B. Scholkopf;T. Schultz
中科院分区：
文献类型：
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作者：
Mohammad Khatami;T. Schmidt-Wilcke;P. Sundgren;Amin Abbasloo;B. Scholkopf;T. Schultz
弥散MRI （Diffusion MRI, dMRI）提供了关于人脑白质的丰富信息，使我们能够深入了解神经系统疾病、正常衰老和神经可塑性。我们提出了BundleMAP，一种从dMRI数据中提取特征的方法，可用于监督分类、回归和假设检验。我们的特征是基于沿神经纤维束聚集测量，使可视化和解剖解释。BundleMAP背后的主要思想是使用ISOMAP流形学习技术来联合参数化神经纤维束。我们将这个想法与异常值去除和特征选择机制结合起来，以获得一个实用的机器学习管道。我们证明了它提高了疾病检测和疾病活动估计的准确性，并且提高了统计检验的能力。
Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.