Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression

  • Viktor WegmayrEmail author
  • Maurice Hörold
  • Joachim M. Buhmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)


Predicting the age progression of individual brain images from longitudinal data has been a challenging problem, while its solution is considered key to improve dementia prognosis. Often, approaches are limited to group-level predictions, lack the ability to extrapolate, can not scale to many samples, or do not operate directly on image inputs. We address these issues with the first approach to artificial aging of brain images based on Wasserstein Generative Adversarial Networks. We develop a novel recursive generator model for brain image time series, and train it on large-scale longitudinal data sets (ADNI/AIBL). In addition to thorough analysis of results on healthy and demented subjects, we demonstrate the predictive value of our brain aging model in the context of conversion prognosis from mild cognitive impairment to Alzheimer’s disease. Conversion prognosis for a baseline image is achieved in two steps. First, we estimate the future brain image with the Generative Adversarial Network. This follow-up image is passed to a CNN classifier, pre-trained to discriminate between mild cognitive impairment and Alzheimer’s disease. It estimates the Alzheimer probability for the follow-up image, which represents an effective measure for future disease risk.


WGAN Deep learning Brain aging Alzheimer MRI 


  1. 1.
    Alberdi, A., et al.: On the early diagnosis of Alzheimer’s Disease from multimodal signals: a survey. Artif. Intell. Med. 71, 1–29 (2016)CrossRefGoogle Scholar
  2. 2.
    Antipov, G., Baccouche, M., Dugelay, J.L.: Face aging with conditional generative adversarial networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2089–2093 (2017)Google Scholar
  3. 3.
    Giorgio, A., et al.: Age-related changes in grey and white matter structure throughout adulthood. In: NeuroImage (2010)Google Scholar
  4. 4.
    Cheng, B., et al.: Domain transfer learning for MCI conversion prediction. IEEE Trans. Biomed. Eng. 62, 1805–1817 (2012)CrossRefGoogle Scholar
  5. 5.
    Bône, A., et al.: Prediction of the progression of subcortical brain structures in Alzheimer’s disease from baseline. In: GRAIL/MFCA/MICGen@MICCAI (2017)Google Scholar
  6. 6.
    Cabral, C., et al.: Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput. Biol. Med. 58, 101–109 (2015)CrossRefGoogle Scholar
  7. 7.
    Baumgartner, C.F., et al.: Visual feature attribution using wasserstein GANs. In: CVPR (2018)Google Scholar
  8. 8.
    Bowles, C., et al.: Modelling the progression of Alzheimer’s disease in MRI using generative adversarial networks. In: Medical Imaging: Image Processing (2018)Google Scholar
  9. 9.
    Jack, C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging JMRI 27, 685–691 (2008)CrossRefGoogle Scholar
  10. 10.
    Ding, Z., Fleishman, G., Yang, X., Thompson, P., Kwitt, R., Niethammer, M.: Fast predictive simple geodesic regression. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 267–275. Springer, Cham (2017). CrossRefGoogle Scholar
  11. 11.
    Lu, D., et al.: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. In: Scientific Reports (2018)Google Scholar
  12. 12.
    Moradi, E., et al.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage 104, 398–412 (2015)CrossRefGoogle Scholar
  13. 13.
    Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  14. 14.
    Korolev, I.O., et al.: Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. In: PloS One (2016)Google Scholar
  15. 15.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976 (2017)Google Scholar
  16. 16.
    Dukart, J., et al.: Generative FDG-PET and MRI model of aging and disease progression in Alzheimer’s disease. PLoS Comput. Biol. 9(4), e1002987 (2013)CrossRefGoogle Scholar
  17. 17.
    Ellis, K.A., et al.: The Australian 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. Psychoger. 21, 672–687 (2009)CrossRefGoogle Scholar
  18. 18.
    Lin, W., et al.: Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front. Neurosci. (2018)Google Scholar
  19. 19.
    Liu, S., Sun, Y., Zhu, D., Bao, R., Wang, W., Shu, X., Yan, S.: Face aging with contextual generative adversarial nets. In: ACM Multimedia (2017)Google Scholar
  20. 20.
    Arjovsky, M., et al.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 70, pp. 214–223 (2017)Google Scholar
  21. 21.
    Mueller, S.G., et al.: Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s disease neuroimaging initiative (ADNI). Alzheimer’s Dement. 1, 55–66 (2005)CrossRefGoogle Scholar
  22. 22.
    Niethammer, M., Huang, Y., Vialard, F.-X.: Geodesic regression for image time-series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011). Scholar
  23. 23.
    Palsson, S.: Generative adversarial style transfer networks for face aging. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2165–21658 (2018)Google Scholar
  24. 24.
    Pathan, S., Hong, Y.: Predictive image regression for longitudinal studies with missing data. In: Medical Imaging with Deep Learning (MIDL) (2018)Google Scholar
  25. 25.
    Peters, R.: Ageing and the brain. Postgrad. Med. J. 82(964), 84–88 (2006)CrossRefGoogle Scholar
  26. 26.
    Querbes, O., et al.: Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain J. Neurol. 132, 2036–2047 (2009)CrossRefGoogle Scholar
  27. 27.
    Resnick, S.M., Pham, D.L., Kraut, M.A., Zonderman, A.B., Davatzikos, C.: Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J. Neurosci. Official J. Soc. Neurosci. 23(8), 3295–3301 (2003)CrossRefGoogle Scholar
  28. 28.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Bildverarbeitung für die Medizin (2017)Google Scholar
  29. 29.
    Wegmayr, V., Hörold, M., Buhmann, J.M.: Generative aging of brain MRI for early prediction of MCI-AD conversion. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (2019)Google Scholar
  30. 30.
    Wolz, R., et al.: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. In: PloS One (2011)Google Scholar
  31. 31.
    Huizinga, W., et al.: A spatio-temporal reference model of the aging brain. NeuroImage 169, 11–22 (2018)CrossRefGoogle Scholar
  32. 32.
    Yang, H., Huang, D., Wang, Y., Jain, A.K.: Learning face age progression: a pyramid architecture of GANs. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 31–39 (2018)Google Scholar
  33. 33.
    Sun, Z., et al.: Detection of conversion from mild cognitive impairment to Alzheimer’s disease using longitudinal brain MRI. Front. Neuroinform. (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Viktor Wegmayr
    • 1
    Email author
  • Maurice Hörold
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.ETH ZurichZurichSwitzerland

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