论文标题
通过深度学习的生物力学建模
Biomechanical modelling of brain atrophy through deep learning
论文作者
论文摘要
我们提出了概念验证，深度学习（DL）的逼真的生物力学模型的现实脑变形模型。使用局部萎缩和生长的规定图作为输入，该网络学会根据组织变形的新霍克人模型进行变形图像。使用阿尔茨海默氏病神经成像计划（ADNI）数据集中的纵向脑萎缩数据对该工具进行了验证，我们证明了训练有素的模型能够快速模拟具有最小残留物的新大脑变形。该方法有可能用于数据增强或探索反映脑生长和萎缩的不同因果假设。
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.
