Wade Benjamin S C, Joshi Shantanu H, Pirnia Tara, Leaver Amber M, Woods Roger P, Thompson Paul M, Espinoza Randall, Narr Katherine L
Imaging Genetics Center, University of Southern California.
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA.
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:92-96. doi: 10.1109/ISBI.2015.7163824.
Disorders of the central nervous system are often accompanied by brain abnormalities detectable with MRI. Advances in biomedical imaging and pattern detection algorithms have led to classification methods that may help diagnose and track the progression of a brain disorder and/or predict successful response to treatment. These classification systems often use high-dimensional signals or images, and must handle the computational challenges of high dimensionality as well as complex data types such as shape descriptors. Here, we used shape information from subcortical structures to test a recently developed feature-selection method based on regularized random forests to 1) classify depressed subjects versus controls, and 2) patients before and after treatment with electroconvulsive therapy. We subsequently compared the classification performance of high-dimensional shape features with traditional volumetric measures. Shape-based models outperformed simple volumetric predictors in several cases, highlighting their utility as potential automated alternatives for establishing diagnosis and predicting treatment response.
中枢神经系统疾病常常伴有可通过磁共振成像（MRI）检测到的脑部异常。生物医学成像和模式检测算法的进步催生了一些分类方法，这些方法可能有助于诊断和跟踪脑部疾病的进展以及预测对治疗的成功反应。这些分类系统通常使用高维信号或图像，并且必须应对高维度带来的计算挑战以及诸如形状描述符等复杂数据类型。在此，我们利用来自皮层下结构的形状信息，测试一种基于正则化随机森林的最新开发的特征选择方法，以1）对抑郁症患者与对照组进行分类，以及2）对接受电休克治疗前后的患者进行分类。随后，我们将高维形状特征的分类性能与传统的体积测量方法进行了比较。在几种情况下，基于形状的模型优于简单的体积预测器，凸显了其作为建立诊断和预测治疗反应的潜在自动化替代方法的效用。
