Supervised brain segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder

L. Igual, J. Soliva, Antonio Hernández-Vela, Sergio Escalera, Ó. Vilarroya, P. Radeva
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引用次数: 9

Abstract

This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation.
监督脑分割与分类在注意缺陷/多动障碍诊断中的应用
本文提出了一种基于统计和结构机器学习方法的核磁共振图像尾状核内部和外部自动分割方法。该方法应用于注意力缺陷/多动障碍(ADHD)的诊断。外部分割方法采用了Graph Cut能量最小化模型,使其适合分割小的、低对比度的结构,如尾状核。特别地,定义了新的能量函数数据和边界势,并添加了基于上下文脑结构的监督能量项。此外,内部分割方法根据MRI切片中感兴趣区域(ROI)的形状特征学习分类器。结果表明,该方法在真实数据集上实现了准确的内外尾状核分割,ADHD诊断测试的性能与人工标注相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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