Cells interact mechanically with their surrounding by exerting forces and sensing forces or force-induced displacements. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiology. However, to solve the ill-posted mathematical problem, its implementation has involved regularization and the associated compromises in accuracy and resolution. Here we applied neural network-based deep learning as a novel approach for TFM. We modified a network for processing images to process vector fields of stress and strain. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and strains for training the network. We found that deep learning-based TFM yielded results qualitatively similar to those from conventional methods but at a higher accuracy and resolution. The speed and performance of deep learning TFM make it an appealing alternative to conventional methods for characterizing mechanical interactions between cells and the environment.

中文翻译：

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深度学习的牵引力显微镜

细胞通过施加力和感应力或力引起的位移与周围环境机械相互作用。牵引力显微镜（TFM）旨在绘制细胞产生的力或应力，它是一种重要的工具，已推动了机械生物学的迅速发展。但是，为了解决张贴错误的数学问题，其实现涉及正则化以及准确性和分辨率方面的折衷。在这里，我们将基于神经网络的深度学习作为TFM的一种新方法。我们修改了用于处理图像的网络，以处理应力和应变的矢量场。此外，我们调整了用于细胞迁移的数学模型，以生成用于训练网络的大量模拟应力和应变。我们发现，基于深度学习的TFM产生的结果在质量上与传统方法相似，但准确性和分辨率更高。深度学习TFM的速度和性能使其成为表征细胞与环境之间机械相互作用的传统方法的一种有吸引力的替代方法。