Dept. Applied Mathematics and Analysis, Universitat de Barcelona, Gran Via Corts Catalanes 585, 08007 Barcelona, Spain.
Comput Med Imaging Graph. 2012 Dec;36(8):591-600. doi: 10.1016/j.compmedimag.2012.08.002. Epub 2012 Sep 5.
We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.
我们提出了一种基于先前发现的尾状核体积异常的完全自动化的注意力缺陷/多动障碍诊断辅助成像测试。所提出的方法包括不同的步骤：一种新的基于机器学习方法的尾状核外部和内部自动分割方法；一组新的体积关系特征，即 3D 离散偶极子的定义，用于尾状核的表示和分类。我们分别使用儿科人群的真实数据验证了这些贡献，并显示了该诊断测试的精确内部尾状核分割和区分能力，与其他最先进的方法相比，性能有了显著提高。
