期刊
MEDICAL IMAGE ANALYSIS
卷 63, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2020.101694
关键词
Convolutional neural network; Reproducibility; Alzheimer's disease classification Magnetic resonance imaging
类别
资金
- program Investissements d'avenir (Agence Nationale de la Recherche-10-Investissements Avenir Institut Hospitalo-Universitaire-6) [ANR-10-IAIHU-06]
- Agence Nationale de la Recherche -19 -Programme Instituts Interdisciplinaires Intelligence Artificielle-0001, project PRAIRIE [ANR-19-P3IA-0001]
- European Union H2020 program (project EuroPOND) [666992]
- joint NSF/NIH/ANR program Collaborative Research in Computational Neuroscience (project HIPLAY7) [ANR-16-NEUC-0001-01]
- China Scholarship Council (CSC)
- Contrat d'Interface Local from Assistance Publique-Hopitaux de Paris (AP-HP)
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant) [U01 AG024904]
- DOD ADNI (Department of Defense award) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
- [P50 AG05681]
- [P01 AG03991]
- [P01 AG026276]
- [R01 AG021910]
- [P20 MH071616]
- [U24 RR021382]
- Agence Nationale de la Recherche (ANR) [ANR-16-NEUC-0001] Funding Source: Agence Nationale de la Recherche (ANR)
Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL. (C) 2020 Elsevier B.V. All rights reserved.
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