论文标题
基于张量的分级：一种基于补丁的新型分级方法，用于分析亨廷顿氏病中变形场
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease
论文作者
论文摘要
磁共振成像的改进导致了许多技术的发展，以更好地检测由神经退行性疾病引起的结构改变。其中，已经提出了基于斑块的分级框架来建模解剖变化的局部模式。这种方法由于其计算成本低及其竞争性能而具有吸引力。其他研究提出，使用基于张量的形态法分析大脑结构的变形，这是一种高度可解释的方法。在这项工作中，我们建议通过将基于贴片的分级框架与基于张量的分级方法扩展到这两种方法的优点，从而使我们能够使用log-euclidean度量来对局部变形模型进行建模。我们评估了我们的新方法在对猎人前亨廷顿氏病和健康对照的患者分类的壳虫研究中。我们的实验表明，与现有的基于贴片的分级方法相比，分类准确性（87.5 $ \ pm $ 0.5 vs. 81.3 $ \ pm $ 0.6），以及对壳质体积的良好补充，这是用于研究亨廷顿氏病的主要成像标记。
The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Among these, the patch-based grading framework has been proposed to model local patterns of anatomical changes. This approach is attractive because of its low computational cost and its competitive performance. Other studies have proposed to analyze the deformations of brain structures using tensor-based morphometry, which is a highly interpretable approach. In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric. We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls. Our experiments show a substantial increase in classification accuracy (87.5 $\pm$ 0.5 vs. 81.3 $\pm$ 0.6) compared to the existing patch-based grading methods, and a good complement to putamen volume, which is a primary imaging-based marker for the study of Huntington's disease.
