Biomechanical modelling of brain atrophy through deep learning

Mariana da Silva; Kara Garcia; Carole H. Sudre; Cher Bass; M. Jorge Cardoso; Emma Robinson

Biomechanical modelling of brain atrophy through deep learning

Machine Learning 2020-12-15 v1 Computer Vision and Pattern Recognition Image and Video Processing Tissues and Organs Machine Learning

Authors: Mariana da Silva , Kara Garcia , Carole H. Sudre , Cher Bass , M. Jorge Cardoso , Emma Robinson

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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.

Keywords

brain imaging deep learning deep neural networks

Cite

@article{arxiv.2012.07596,
  title  = {Biomechanical modelling of brain atrophy through deep learning},
  author = {Mariana da Silva and Kara Garcia and Carole H. Sudre and Cher Bass and M. Jorge Cardoso and Emma Robinson},
  journal= {arXiv preprint arXiv:2012.07596},
  year   = {2020}
}

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Submitted to Medical Imaging Meets NeurIPS 2020

Biomechanical modelling of brain atrophy through deep learning

Abstract

Keywords

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