Biomechanical modelling of brain atrophy through deep learning

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 u...

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Bibliographic Details
Main Authors da Silva, Mariana, Garcia, Kara, Sudre, Carole H, Bass, Cher, Cardoso, M. Jorge, Robinson, Emma
Format Journal Article
LanguageEnglish
Published 14.12.2020
Subjects
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Quantitative Biology - Tissues and Organs
Statistics - Machine Learning
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Summary: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.
DOI:10.48550/arxiv.2012.07596