IEEE J Biomed Health Inform. 2018 Sep;22(5):1476-1485. doi: 10.1109/JBHI.2018.2791863. Epub 2018 Jan 10.
Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer's disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer's disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer's disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.
大多数用于脑部疾病诊断的自动化技术都利用结构磁共振（MR）图像的手工制作（例如基于体素或基于区域）生物标志物作为特征表示。然而，这些手工制作的特征通常是高维的，或者需要专家定义的感兴趣区域。此外，由于手工制作的特征和后续模型之间可能存在异构性质，因此现有方法可能导致脑部疾病诊断的性能不佳。在本文中，我们提出了一种基于地标（landmark-based）的深度特征学习（LDFL）框架，用于从 MRI 中自动提取基于补丁的表示，以实现阿尔茨海默病的自动诊断。我们首先以数据驱动的方式从 MR 图像中识别出有区别的解剖地标，然后提出了一种基于卷积神经网络的基于补丁的深度特征学习方法。我们已经在三个公共数据集（包括阿尔茨海默病神经影像学倡议（ADNI-1）、ADNI-2 和阿尔茨海默病最小间隔磁共振成像（MIRIAD）数据集）中的受试者上评估了所提出的方法。脑部疾病分类和 MR 图像检索这两个任务的实验结果都表明，所提出的 LDFL 方法提高了疾病分类和 MR 图像检索的性能。
