Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images.
Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images.
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DOI：
10.3389/fninf.2018.00035
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发表时间：
2018
影响因子：
3.5
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Liu M;Cheng D;Yan W;Alzheimer’s Disease Neuroimaging Initiative
阿尔茨海默氏病 (AD) 是一种不可逆的大脑退行性疾病，影响 65 岁以上的人群。目前，AD 尚无有效治愈方法，但通过一些治疗可以延缓其进展。 AD 的准确早期诊断对于患者护理和未来治疗的发展至关重要。氟脱氧葡萄糖正电子发射断层扫描 (FDG-PET) 是一种功能性分子成像方式，被证明可以有效帮助了解与 AD 相关的大脑解剖和神经变化。大多数现有方法从图像中提取手工特征，然后设计一个分类器来区分 AD 和其他群体。这些方法高度依赖于大脑图像的预处理，包括图像刚性配准和分割。受深度学习在图像分类方面成功的启发，本文提出了一种基于 2D 卷积神经网络（CNN）和循环神经网络（RNN）相结合的新分类框架，该框架在将 3D PET 图像分解为 2D 切片序列后学习切片内和切片间特征以进行分类。 2D CNN 旨在捕获图像切片的特征，而 RNN 的门控循环单元 (GRU) 级联以学习和集成切片间特征以进行图像分类。 PET 图像不需要严格的配准和分割。我们的方法基于从阿尔茨海默病神经影像倡议 (ADNI) 数据库中获取的 339 名受试者（包括 93 名 AD 患者、146 名轻度认知障碍 (MCI) 和 100 名正常对照 (NC)）的基线 FDG-PET 图像进行评估。实验结果表明，该方法在 AD 与 NC 分类中实现了 95.3% 的受试者工作特征曲线下面积 (AUC)，在 MCI 与 NC 分类中实现了 83.9%，展示了良好的分类性能。
Alzheimer’s disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG-PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. These methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNNs), which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The 2D CNNs are built to capture the features of image slices while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter-slice features for image classification. No rigid registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG-PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance.
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DOI：
10.1146/annurev-bioeng-071516-044442
发表时间：
2017-06-21
影响因子：
9.7
作者：
Shen D;Wu G;Suk HI
通讯作者：
Suk HI
DOI：
10.1007/978-3-319-10443-0_39
发表时间：
2014
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
通讯作者：
--
影响因子：
5.7
作者：
Liu F;Wee CY;Chen H;Shen D
通讯作者：
Shen D
影响因子：
4.8
作者：
Zhan, Liang;Zhou, Jiayu;Thompson, Paul M.
通讯作者：
Thompson, Paul M.
影响因子：
3.1
作者：
Suk HI;Lee SW;Shen D;Alzheimer’s Disease Neuroimaging Initiative
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative