Unsupervised detection of individual atrophy in Alzheimer's disease

Background: To realize precision medicine, it is important to realize the detection of the individual atrophy of Alzheimer's disease (AD) patients. Our objective is to find individual brain regions of interest (ROIs) in AD patients via an unsupervised deep learning network.Methods: This study u...

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Vydané v:2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Ročník 2021; s. 2647 - 2650
Hlavní autori: Jin, Shichen, Zou, Peini, Han, Ying, Jiang, Jiehui
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2021
Predmet:
Adversarial autoencoders
Alzheimer Disease - diagnosis
Alzheimer's disease
Atrophy
Atrophy detection
Biological system modeling
Brain
Deep learning
Generative adversarial networks
Hospitals
Humans
Magnetic Resonance Imaging
Multilayer perceptron
Neuroimaging
Training
ISSN:2694-0604, 2694-0604
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Shrnutí:Background: To realize precision medicine, it is important to realize the detection of the individual atrophy of Alzheimer's disease (AD) patients. Our objective is to find individual brain regions of interest (ROIs) in AD patients via an unsupervised deep learning network.Methods: This study used structural Magnetic Resonance Imaging (sMRI) scans with the 732 healthy control (HC) subjects and 202 AD patients from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the 105 HC subjects were collected at the Xuanwu Hospital. An unsupervised deep learning network based on Adversarial Autoencoders (AAE) was proposed to delineate the individual atrophy of AD patients. In the proposed model, Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) were combined to learn the potential distribution and train a generator. In this step, the 530 HCs from ADNI were applied as the training dataset and the 105 HCs from Xuanwu Hospital were applied as an external validation dataset. The structural similarity (SSIM) was used to judge the robustness of the proposed model. Then, ROIs of the 202 AD patients were detected. In order to verify the clinical performance of these ROIs, other 202 HCs were selected from ADNI and a multilayer perceptron (MLP) was used to classify AD versus HC by 5 folder cross-validation. In the comparative experiments, we compared our model with three other previous models.Results: The SSIM reached 0.86 in both training and external validation datasets. Eventually, the classification accuracy of our model achieved 0.94±0.02. In the meanwhile, the classification accuracies were 0.89±0.01, 0.85±0.04 and 0.91±0.03 for the three previous methods.Conclusion: Our deep learning model could detect individual atrophy in AD patients. It may be a useful tool for AD diagnosis in clinics.
Bibliografia:ObjectType-Article-1
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ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC46164.2021.9630103