Abstract
Machine learning methods have many promising applications in medical imaging, including the diagnosis of Alzheimer’s Disease (AD) based on magnetic resonance imaging (MRI) brain scans. These scans usually undergo several preprocessing steps, including image registration. However, the effect of image registration methods on the performance of the machine learning classifier is poorly understood. In this study, we train a convolutional neural network (CNN) to detect AD on a dataset preprocessed in three different ways. The scans were registered to a template either linearly or nonlinearly, or were only padded and cropped to the needed size without performing image registration. We show that both linear and nonlinear registration increase the balanced accuracy of the classifier significantly by around 6–7% in comparison to no registration. No significant difference between linear and nonlinear registration was found. The dataset split, although carefully matched for age and sex, affects the classifier performance strongly, suggesting that some subjects are easier to classify than others, possibly due to different clinical manifestations of AD and varying rates of disease progression. In conclusion, we show that for a CNN detecting AD, a prior image registration improves the classifier performance, but the choice of a linear or nonlinear registration method has only little impact on the classification accuracy and can be made based on other constraints such as computational resources or planned further analyses like the use of brain atlases.
M. Klingenberg and D. Stark—These authors contributed equally to this work.
Alzheimer’s Disease Neuroimaging Initiative—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Acknowledgements
We thank Tobias Scheffer for his useful suggestions. We acknowledge support from the German Research Foundation (DFG, 389563835; TRR 265, 402170461; CRC 1404, 414984028), the Brain & Behavior Research Foundation (NARSAD Young Investigator Grant, USA) and the Manfred and Ursula-Müller Stiftung.
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: 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 and its affiliated company 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; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
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Klingenberg, M., Stark, D., Eitel, F., Ritter, K., for the Alzheimer’s Disease Neuroimaging Initiative. (2021). MRI Image Registration Considerably Improves CNN-Based Disease Classification. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham. https://doi.org/10.1007/978-3-030-87586-2_5
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DOI: https://doi.org/10.1007/978-3-030-87586-2_5
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