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Microscopy, Atomic Force

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Speeding up the Topography Imaging of Atomic Force Microscopy by Convolutional Neural Network.

Analytical chemistry
Atomic force microscopy (AFM) provides unprecedented insight into surface topography research with ultrahigh spatial resolution at the subnanometer level. However, a slow scanning rate has to be employed to ensure the image quality, which will largel...

Deep-learning-based 3D cellular force reconstruction directly from volumetric images.

Biophysical journal
The forces exerted by single cells in the three-dimensional (3D) environments play a crucial role in modulating cellular functions and behaviors closely related to physiological and pathological processes. Cellular force microscopy (CFM) provides a f...

Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images.

Communications biology
Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images. The full process can be divided ...

Comparative study of deep learning algorithms for atomic force microscopy image denoising.

Micron (Oxford, England : 1993)
Atomic force microscopy (AFM) enables direct visualisation of surface topography at the nanoscale. However, post-processing is generally required to obtain accurate, precise, and reliable AFM images owing to the presence of image artefacts. In this s...

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data.

Journal of chemical information and modeling
Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consumin...

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

Biophysical journal
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 ad...

Cell recognition based on atomic force microscopy and modified residual neural network.

Journal of structural biology
Cell recognition methods are in high demand in cell biology and medicine, and the method based on atomic force microscopy (AFM) shows a great value in application. The difference in mechanical properties or morphology of cells has been frequently use...

Artificial Neural Network-Aided Computational Approach for Mechanophenotyping of Biological Cells Using Atomic Force Microscopy.

Journal of biomechanical engineering
The artificial neural network (ANN) based models have shown the potential to provide alternate data-driven solutions in disease diagnostics, cell sorting and overcoming AFM-related limitations. Hertzian model-based prediction of mechanical properties...

Deep Learning Image Recognition-Assisted Atomic Force Microscopy for Single-Cell Efficient Mechanics in Co-culture Environments.

Langmuir : the ACS journal of surfaces and colloids
Atomic force microscopy (AFM)-based force spectroscopy assay has become an important method for characterizing the mechanical properties of single living cells under aqueous conditions, but a disadvantage is its reliance on manual operation and exper...

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.

Nature
Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information...