Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.

中文翻译：

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使用结构 MRI 进行关节萎缩定位和阿尔茨海默病诊断的分层全卷积网络


结构磁共振成像（sMRI）由于其对脑萎缩引起的形态变化敏感，已广泛用于神经退行性疾病（例如阿尔茨海默氏病（AD））的计算机辅助诊断。最近，一些深度学习方法（例如，卷积神经网络，CNN）被提出来从 sMRI 中学习面向任务的特征来进行 AD 诊断，并且比使用手工制作特征的传统基于学习的方法取得了更好的性能。然而，这些现有的基于 CNN 的方法仍然需要预先确定 sMRI 中的信息位置。也就是说，判别性萎缩定位阶段与特征提取和分类器构建的后期阶段是隔离的。在本文中，我们提出了一种分层全卷积网络（H-FCN）来自动识别全脑 sMRI 中的判别性局部斑块和区域，然后联合学习和融合多尺度特征表示以构建 AD 的分层分类模型诊断。我们提出的 H-FCN 方法在来自两个独立数据集（即 ADNI-1 和 ADNI-2）的大量受试者上进行了评估，证明在联合判别性萎缩定位和脑部疾病诊断方面具有良好的性能。