Enhancing robustness, precision, and speed of traction force microscopy with machine learning.

ABSTRACT: Traction patterns of adherent cells provide important information on their interaction with the environment, cell migration, or tissue patterns and morphogenesis. Traction force microscopy is a method aimed at revealing these traction patterns for adherent cells on engineered substrates with known constitutive elastic properties from deformation information obtained from substrate images. Conventionally, the substrate deformation information is processed by numerical algorithms of varying complexity to give the corresponding traction field via solution of an ill-posed inverse elastic problem. We explore the capabilities of a deep convolutional neural network as a computationally more efficient and robust approach to solve this inversion problem. We develop a general purpose training proces

SUBMITTER: Kratz FS 

PROVIDER: S-EPMC10502481 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature