Hong Yi, Gao Yi, Niethammer Marc, Bouix Sylvain
Department of Computer Science, University of North Carolina (UNC) at Chapel Hill, NC, USA.
Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
Med Image Anal. 2015 Oct;25(1):2-10. doi: 10.1016/j.media.2015.04.004. Epub 2015 Apr 16.
In this paper we propose a new method for shape analysis based on the ordering of shapes using band-depth. We use this band-depth to non-parametrically define a global depth for a shape with respect to a reference population, typically consisting of normal control subjects. This allows us to globally quantify differences with respect to "normality". Using the depth-ordering of shapes also allows the detection of localized shape differences by using α-central values of shapes. We propose permutation tests to statistically assess global and local shape differences. We further determine the directionality of shape differences (local inflation versus deflation). The method is evaluated on a synthetically generated striatum dataset, and applied to detect shape differences in the hippocampus between subjects with first-episode schizophrenia and normal controls.
在本文中，我们提出了一种基于形状排序的新方法，该方法利用带深度对形状进行分析。我们使用这种带深度来非参数地定义一个形状相对于参考总体（通常由正常对照受试者组成）的全局深度。这使我们能够全局量化相对于“正常性”的差异。利用形状的深度排序还可以通过使用形状的α中心值来检测局部形状差异。我们提出了置换检验来统计评估全局和局部形状差异。我们进一步确定形状差异的方向性（局部膨胀与收缩）。该方法在一个合成生成的纹状体数据集上进行了评估，并应用于检测首发精神分裂症患者与正常对照者海马体中的形状差异。
