Title: In Silico Prediction of Cell Traction Forces Show Chinese title

Title: 计算机模拟预测细胞牵引力

Abstract: Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface. Typically, TFM requires culturing cells on gels with fluorescent beads, followed by bead displacement calculations. We present a new method allowing to predict those forces from a regular fluorescent image of the cell. Using Deep Learning, we trained a Bayesian Neural Network adapted for pixel regression of the forces and show that it generalises on different cells of the same strain. The predicted forces are computed along with an approximated uncertainty, which shows whether the prediction is trustworthy or not. Using the proposed method could help estimating forces when calculating non-trivial bead displacements and can also free one of the fluorescent channels of the microscope. Code is available at \url{https://github.com/wahlby-lab/InSilicoTFM}. Show Chinese abstract

Abstract: 牵引力显微镜（TFM）是一种用于确定生物细胞施加于底层表面的张力的技术。 通常，TFM需要在含有荧光珠的凝胶上培养细胞，然后进行珠子位移计算。 我们提出了一种新方法，可以从细胞的常规荧光图像中预测这些力。 利用深度学习，我们训练了一个适用于像素回归的贝叶斯神经网络，并表明它可以在同一菌株的不同细胞上进行泛化。 预测的力同时伴随着一个近似的不确定性，这表明预测是否可靠。 使用所提出的方法可以在计算非平凡的珠子位移时帮助估计力，并且还可以使显微镜的一个荧光通道得到释放。 代码可在\url{https://github.com/wahlby-lab/InSilicoTFM}中获得。