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Manifold Learning for Biomarker Discovery in MR Imaging

机译：在MR成像中生物标记发现的流形学习

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摘要

We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter- and intra-subject brain variation in MR image data. The coordinates of each image in such a low-dimensional space captures information about structural shape and appearance and, when a phenotype exists, about the subject's clinical state. A key contribution is that we propose a method for incorporating longitudinal image information in the learned manifold. In particular, we compare simultaneously embedding baseline and follow-up scans into a single manifold with the combination of separate manifold representations for inter-subject and intra-subject variation. We apply the proposed methods to 362 subjects enrolled in the Alzheimer's Disease Neu-roimaging Initiative (ADNI) and classify healthy controls, subjects with Alzheimer's disease (AD) and subjects with mild cognitive impairment (MCI). Learning manifolds based on both the appearance and temporal change of the hippocampus, leads to correct classification rates comparable with those provided by state-of-the-art automatic segmentation estimates of hippocampal volume and atrophy. The biomarkers identified with the proposed method are data-driven and represent a potential alternative to a-priori defined biomarkers derived from manual or automated segmentations.
机译：我们提出了从低维流形提取生物标记的框架，该流形代表了MR图像数据中受试者间和受试者内脑部变异。在这样的低维空间中，每个图像的坐标捕获有关结构形状和外观的信息，以及当存在表型时有关受试者的临床状态的信息。一个关键的贡献是，我们提出了一种在学习的歧管中合并纵向图像信息的方法。特别是，我们比较了将基线扫描和后续扫描同时嵌入到单个流形中的情况，并将各个流形表示形式的组合用于对象间和对象内变化。我们将拟议的方法应用于362名参加阿尔茨海默氏病神经成像倡议（ADNI）的受试者，并对健康对照，患有阿尔茨海默氏病（AD）和轻度认知障碍（MCI）的受试者进行分类。基于海马的外观和时间变化的学习流形，可得出与最新的海马体积和萎缩自动分段估计所提供的正确分类率相当的正确分类率。用提出的方法鉴定的生物标志物是数据驱动的，并且代表了从手动或自动分割中衍生的先验定义的生物标志物的潜在替代物。

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  • 来源
    《Machine learning in medical imaging》|2010年|p.116-123|共8页
  • 会议地点 Beijing(CN);Beijing(CN);Beijing(CN);Beijing(CN)
  • 作者

    Robin Wolz; Paul Aljabar; Joseph V. Hajnal; Daniel Rueckert;

  • 作者单位

    Department of Computing, Imperial College London, London, UK;

    Department of Computing, Imperial College London, London, UK;

    MRC Clinical Sciences Center, Imperial College London, London, UK;

    Department of Computing, Imperial College London, London, UK;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医用物理学;
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