ArticleBiophysical journal2021
Traction force microscopy by deep learning.
Article in Biophysical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Guidance for 3D traction force microscopy today and in the next decade.Nature methods · 2026Review
- Deep learning reveals how cells pull, buckle, and navigate fibrous environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A microfluidic rheometer for tumor mechanics and invasion studies.Lab on a chip · 2025Article
- A microfluidic rheometer for tumor mechanics and invasion studies.bioRxiv : the preprint server for biology · 2025Article
- Inferring cellular contractile forces and work using deep morphology traction microscopy.Biophysical journal · 2024Article
- 3D Traction Force Microscopy in Biological Gels: From Single Cells to Multicellular Spheroids.Annual review of biomedical engineering · 2024Review
- Field Guide to Traction Force Microscopy.Cellular and molecular bioengineering · 2024Review
- Natural Killer Cell Mechanosensing in Solid Tumors.Bioengineering (Basel, Switzerland) · 2024Review
- High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy.Communications biology · 2024Article
- Article
- Enhancing robustness, precision, and speed of traction force microscopy with machine learning.Biophysical journal · 2023Article
- Deep-learning-based 3D cellular force reconstruction directly from volumetric images.Biophysical journal · 2022Article
- Functions and clinical significance of mechanical tumor microenvironment: cancer cell sensing, mechanobiology and metastasis.Cancer communications (London, England) · 2022Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Cells interact mechanically with their surroundings by exerting and sensing forces. 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-posed mathematical problem, conventional TFM involved compromises in accuracy and/or resolution. Here, we applied neural network-based deep learning as an alternative approach for TFM. We modified a neural network designed for image processing to predict the vector field of stress from displacements. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and displacements for training and testing the neural network. We found that deep learning-based TFM yielded results that resemble those using conventional TFM but at a higher accuracy than several conventional implementations tested. In addition, a trained neural network is appliable to a wide range of conditions, including cell size, shape, substrate stiffness, and traction output. The performance of deep learning-based TFM makes it an appealing alternative to conventional methods for characterizing mechanical interactions between adherent cells and the environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.