Abstract
Alzheimer’s disease and Frontotemporal dementia are two major types of dementia. Their accurate diagnosis and differentiation is crucial for determining specific intervention and treatment. However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms. Therefore, the automatic classification of multiple types of dementia has an important clinical value. So far, this challenge has not been actively explored. Recent development of deep learning in the field of medical image has demonstrated high performance for various classification tasks. In this paper, we propose to take advantage of two types of biomarkers: structure grading and structure atrophy. To this end, we propose first to train a large ensemble of 3D U-Nets to locally discriminate healthy versus dementia anatomical patterns. The result of these models is an interpretable 3D grading map capable of indicating abnormal brain regions. This map can also be exploited in various classification tasks using graph convolutional neural network. Finally, we propose to combine deep grading and atrophy-based classifications to improve dementia type discrimination. The proposed framework showed competitive performance compared to state-of-the-art methods for different tasks of disease detection and differential diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Available at https://ida.loni.usc.edu/.
- 2.
References
Alladi, S., et al.: Focal cortical presentations of Alzheimer’s disease. Brain 130, 2636–2645 (2007)
Avants, B.B., et al.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011)
Bang, J., et al.: Frontotemporal dementia. The Lancet 386, 1672–1682 (2015)
Brambati, S.M., et al.: A tensor based morphometry study of longitudinal gray matter contraction in FTD. Neuroimage 35, 998–1003 (2007)
Bron, E.E., et al.: Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI. Eur. Radiol. 27, 3372–3382 (2017)
Coupé, P., et al.: Lifespan changes of the hum brain in Alzheimer’s disease. Sci. Rep. 9, 3998 (2019)
Coupé, P., et al.: AssemblyNet: a large ensemble of CNNs for 3D whole brain MRI segmentation. Neuroimage 219, 117026 (2020)
Davatzikos, C., et al.: Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage 41, 1220–1227 (2008)
Du, A.T., et al.: Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130, 1159–1166 (2006)
Ellis, K.A., et al.: The Australi Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int. Psychogeriatr. 21, 672–687 (2009)
Frisoni, G.B., et al.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6, 67–77 (2010)
Harper, L., et al.: An algorithmic approach to structural imaging in dementia. J. Neurol. Neurosurg. Psychiatry 85, 692–698 (2014)
Hu, J., et al.: Deep learning-based classification and voxel-based visualization of frontotemporal dementia and Alzheimer’s disease. Front. Neurosci. 14, 626154 (2021)
Hutchinson, A.D., et al.: Neuropsychological deficits in frontotemporal dementia and Alzheimer’s disease: a meta-analytic review. J. Neurol. Neurosurg. Psychiatry 78, 917–928 (2007)
Jack, C.R., et al.: The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691 (2008)
de Jong, L.W., et al.: Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain 131, 3277–3285 (2008)
Kesslak, J.P., et al.: Quantification of magnetic resonance scans for hippocampal and parahippocampal atrophy in Alzheimer’s disease. Neurology 41, 51–54 (1991)
Kim, J.P., et al.: Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer’s disease. NeuroImage Clin. 23, 101811 (2019)
Kipf, T.N., et al.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017 (2017)
LaMontagne, P.J., et al.: OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. Radiol. Imaging (2019)
Lebedeva, A.K., et al.: MRI-based classification models in prediction of mild cognitive impairment and dementia in late-life depression. Front. Aging Neurosci. 9, 13 (2017)
Ma, D., et al.: Differential diagnosis of frontotemporal dementia, Alzheimer’s disease, and normal aging using a multi-scale multi-type feature generative adversarial deep neural network on structural magnetic resonance images. Front. Neurosci. 14, 853 (2020)
Malone, I.B., et al.: MIRIAD-public release of a multiple time point Alzheimer’s MR imaging dataset. Neuroimage 70, 33–36 (2013)
Manjón, J.V., et al.: Robust MRI brain tissue parameter estimation by multistage outlier rejection. Magn. Reson. Med. 59, 866–873 (2008)
Manjón, J.V., et al.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)
Manjón, J.V., et al.: Nonlocal intracranial cavity extraction. Int. J. Biomed. Imaging 2014, 820205 (2014)
McKhann, G.M., et al.: The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 7, 263–269 (2011)
Möller, C., et al.: Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis. Radiology 279, 838–848 (2016)
Neary, D., et al.: Frontotemporal dementia. Lancet Neurol. 4, 771–780 (2005)
Nguyen, H.D., et al.: Deep grading based on collective artificial intelligence for AD diagnosis and prognosis. In: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data, vol. 12929, pp. 24–33 (2021)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rabinovici, G., et al.: Distinct MRI atrophy patterns in autopsy-proven Alzheimer’s disease and frontotemporal lobar degeneration. Am. J. Alzheimer’s Dis. Other Dement. 22, 474–488 (2008)
Rascovsky, K., et al.: Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134, 2456–2477 (2011)
Rosen, H.J., et al.: Patterns of brain atrophy in frontotemporal dementia and semantic dementia. Neurology 58, 198–208 (2002)
Rosen, H.J., et al.: Neuroanatomical correlates of behavioural disorders in dementia. Brain 128, 2612–2625 (2005)
Schuff, N., et al.: MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain 132, 1067–1077 (2009)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)
Whitehouse, P., et al.: Alzheimer’s disease and senile dementia: loss of neurons in the basal forebrain. Science 215, 1237–1239 (1982)
Wong, S., et al.: Contrasting prefrontal cortex contributions to episodic memory dysfunction in behavioural variant frontotemporal dementia and Alzheimer’s disease. PLoS ONE 9, e87778 (2014)
Yew, B., et al.: Lost and forgotten? Orientation versus memory in Alzheimer’s disease and frontotemporal dementia. J. Alzheimer’s Dis. JAD 33, 473–481 (2013)
Yu, Q., et al.: An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimer’s Res. Therapy 13, 23 (2021)
Acknowledgments
This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02 and RRI “IMPACT”), the French Ministry of Education and Research, and the CNRS for DeepMultiBrain project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, HD., Clément, M., Mansencal, B., Coupé, P. (2022). Interpretable Differential Diagnosis for Alzheimer’s Disease and Frontotemporal Dementia. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-16431-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16430-9
Online ISBN: 978-3-031-16431-6
eBook Packages: Computer ScienceComputer Science (R0)
