Abstract
Brain ageing is associated with morphological changes and cognitive degeneration, and can be affected by neurodegenerative diseases which can accelerate the ageing process. The ability to separate accelerated from healthy ageing is useful from a diagnostic perspective and towards developing subject-specific models of progression. In this paper we start with the ‘simpler’ problem of synthesising age-progressed 2D slices. We adopt adversarial training to learn the joint distribution of brain images and ages, and simulate aged images by a network conditioned on age (a continuous variable) encoded as an ordinal embedding vector. We introduce a loss to help preserve subject identity despite that we train with cross-sectional (unpaired) data. To evaluate the quality of aged images, a pre-trained age predictor is used to estimate an apparent age. We show qualitatively and quantitatively that our method can progressively synthesise realistic brain images of different target ages.
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Acknowledgements
This work was supported by the University of Edinburgh by a PhD studentship. This work was partially supported by EPSRC (EP/P022928/1) and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. This work was supported in part by the US National Institutes of Health (R01HL136578). We also thank Nvidia for donating a Titan-X GPU. S.A. Tsaftaris acknowledges the support of the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme.
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Xia, T., Chartsias, A., Tsaftaris, S.A., for the Alzheimer’s Disease Neuroimaging Initiative. (2019). Consistent Brain Ageing Synthesis. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_82
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DOI: https://doi.org/10.1007/978-3-030-32251-9_82
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