Biomechanical modelling of brain atrophy through deep learning

• URL: http://arxiv.org/abs/2012.07596v1
• Date: Mon, 14 Dec 2020 14:40:47 GMT
• Title: Biomechanical modelling of brain atrophy through deep learning
• Authors: Mariana da Silva, Kara Garcia, Carole H. Sudre, Cher Bass, M. Jorge Cardoso, Emma Robinson
• Abstract summary: The tool is validated using longitudinal brain atrophy data from the Alzheimer's Disease Neuroimaging Initiative dataset. We demonstrate that the trained model is capable of rapidly simulating new brain deformations with minimal residuals. This method has the potential to be used in data augmentation or for the exploration of different causal hypotheses reflecting brain growth and atrophy.
• Score: 4.67368836235476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as input, the network learns to deform images according to a Neo-Hookean model of tissue deformation. The tool is validated using longitudinal brain atrophy data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and we demonstrate that the trained model is capable of rapidly simulating new brain deformations with minimal residuals. This method has the potential to be used in data augmentation or for the exploration of different causal hypotheses reflecting brain growth and atrophy.

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