As a rare neurodegenerative disorder, multiple system atrophy (MSA) can be challenging to analyse using a deep learning approach given the limited sample size. A method is submitted to produce cluster-based simulated brain MR parametric maps from healthy controls, using regional intensity distribution belonging to a set of MSA patients. This enabled to train a 3D CNN only with the simulated set. Testing on the MSA data set, the accuracy obtained was comparable to the state-of-the-art. This approach allows to deal with small samples of data in deep learning, while exploiting a-priori knowledge of the disease.
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