Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI
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DOI：
10.1109/tpami.2018.2889096
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发表时间：
2020-04-01
影响因子：
23.6
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Lian, Chunfeng;Liu, Mingxia;Shen, Dinggang
结构磁共振成像（sMRI）由于其对脑萎缩引起的形态学变化的敏感性，已广泛应用于神经退行性疾病的计算机辅助诊断，如阿尔茨海默病（AD）。最近，一些深度学习方法（如卷积神经网络，cnn）被提出从sMRI中学习面向任务的特征用于AD诊断，并且比传统的基于学习的方法使用手工制作的特征取得了更好的性能。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，判别萎缩定位阶段被隔离到特征提取和分类器构建的后期阶段。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，然后在此基础上联合学习和融合多尺度特征表示，构建分层分类模型用于AD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）的大队列受试者中进行了评估，在关节鉴别萎缩定位和脑部疾病诊断方面表现出良好的性能。
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.