Abstract
Alzheimer’s disease and Frontotemporal dementia are common types of neurodegenerative disorders that present overlapping clinical symptoms, making their differential diagnosis very challenging. Numerous efforts have been done for the diagnosis of each disease but the problem of multi-class differential diagnosis has not been actively explored. In recent years, transformer-based models have demonstrated remarkable success in various computer vision tasks. However, their use in disease diagnostic is uncommon due to the limited amount of 3D medical data given the large size of such models. In this paper, we present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer’s disease and Frontotemporal dementia. Moreover, to overcome the problem of data scarcity, we propose an efficient combination of various data augmentation techniques, adapted for training transformer-based models on 3D structural magnetic resonance imaging data. Finally, we propose to combine our transformer-based model with a traditional machine learning model using brain structure volumes to better exploit the available data. Our experiments demonstrate the effectiveness of the proposed approach, showing competitive results compared to state-of-the-art methods. Moreover, the deformable patch locations can be visualized, revealing the most relevant brain regions used to establish the diagnosis of each disease.
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/.
References
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)
Beekly, D.L., et al.: The National Alzheimer’s Coordinating Center (NACC) database: the uniform data set. Alzheimer Disease Associat. Disord. 21, 249–258 (2007)
Boeve, B.F., et al.: Advances and controversies in frontotemporal dementia: diagnosis, biomarkers, and therapeutic considerations. Lancet Neurol. 21, 258–272 (2022)
Brambati, S.M., et al.: A tensor based morphometry study of longitudinal gray matter contraction in FTD. Neuroimage 35(3), 998–1003 (2007)
Coupé, P., et al.: Lifespan changes of the human 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)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)
Du, A.T., et al.: Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130, 1159–1166 (2006)
Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUS). arXiv preprint arXiv:1606.08415 (2016)
Hu, J., et al.: Deep learning-based classification and voxel-based visualization of frontotemporal dementia and Alzheimer’s disease. Front. Neurosci. 14, 626154 (2021)
Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. In: AMIA Annual Symposium Proceedings, vol. 2017, p. 979 (2017)
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)
Jang, J., Hwang, D.: M3t: three-dimensional Medical image classifier using multi-plane and multi-slice transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20718–20729 (2022)
Li, C., et al.: Trans-ResNet: integrating transformers and CNNs for Alzheimer’s disease classification. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5 (2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Lyu, Y., et al.: Classification of Alzheimer’s disease via vision transformer. In: Proceedings of the 15th International Conference on Pervasive Technologies Related to Assistive Environments, pp. 463–468 (2022)
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)
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: spatially adaptive nonlocal denoising. J. Magn. Reson. Imaging 31, 192–203 (2010)
Manjón, J.V., et al.: Nonlocal intracranial cavity extraction. Int. J. Biomed. Imaging 2014, 1–11 (2014)
Möller, C., et al.: Alzheimer disease and behavioral variant frontotemporal dementia: automatic cassification based on cortical atrophy for single-subject diagnosis. Radiology 279, 838–848 (2016)
Nalepa, J., Marcinkiewicz, M., Kawulok, M.: Data augmentation for brain-tumor segmentation: a review. Front. Comput. Neurosci. 13, 83 (2019)
Nguyen, H., et al.: Interpretable differential diagnosis for Alzheimer’s disease and frontotemporal dementia. In: Medical Image Computing and Computer Assisted Intervention, pp. 61–69 (2022)
Pini, L., et al.: Brain atrophy in Alzheimer’s disease and aging. Ageing Res. Rev. 30, 25–48 (2016)
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(2), 198–208 (2002)
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)
Silhan, D., et al.: The parietal atrophy score on brain magnetic resonance imaging is a reliable visual scale. Curr. Alzheimer Res. 17(6), 534–539 (2020)
Touvron, H., Vedaldi, A., Douze, M., Jégou, H.: Fixing the train-test resolution discrepancy. Adv. Neural Inf. Processing Syst. 32 (2019)
Touvron, H., et al.: Training data-efficient image transformers and distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357 (2021)
Touvron, H., Cord, M., El-Nouby, A., Verbeek, J., Jégou, H.: Three things everyone should know about vision transformers. In: Avidan, S., et al. (eds.) Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23–27 October 2022, Proceedings, Part XXIV, pp. 497–515. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20053-3_29
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)
Whitwell, J.L., et al.: Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study. Brain 132, 2932–2946 (2009)
Xia, Z., et al.: Vision transformer with deformable attention. In: Conference on Computer Vision and Pattern Recognition, pp. 4794–4803 (2022)
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)
Zhang, H., et al.: mixup: beyond empirical risk minimization. arXiv:1710.09412 (2018)
Zhang, S., et al.: 3D Global Fourier Network for Alzheimer’s disease diagnosis using structural MRI. In: Medical Image Computing and Computer Assisted Intervention, pp. 34–43 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 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. (2024). 3D Transformer Based on Deformable Patch Location for Differential Diagnosis Between Alzheimer’s Disease and Frontotemporal Dementia. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-45676-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45675-6
Online ISBN: 978-3-031-45676-3
eBook Packages: Computer ScienceComputer Science (R0)
