Abstract
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.
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Wegmayr, V., Hörold, M., Buhmann, J.M. (2019). Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_17
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DOI: https://doi.org/10.1007/978-3-030-33676-9_17
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