Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Demonstration of partially transparent thick metallic sodium in the vacuum ultraviolet spectral range: comment.

Optics express·2026

Same author

Short-range order in high entropy carbides.

Nature communications·2026

Same author

Tartrate-Assisted Ionic Layer Epitaxy for General Synthesis of 2D Nanostructures.

Small methods·2025

Same author

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.

Machine learning: science and technology·2025

Same author

SuperSalt: equivariant neural network force fields for multicomponent molten salts system.

Nature communications·2025

Same author

Tradipitant Effective in the Reduction of Vomiting Associated with Motion Sickness Across Varied Sea Conditions (S3.003).

Neurology·2025

Same journal

Polyalthia Longifolia Induced Apoptosis via miR-484 Downregulation: A Multimodal In Situ Microscopy, In Vitro, and In Vivo Investigation.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026

Same journal

Rhythmic Pattern of the Ovarian Development in Posthatching Japanese Quail (Coturnix coturnix japonica): Histological, Ultrastructural, and Immunohistochemical Study.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026

Same journal

Postnatal Developmental Expression and Localization of Water Channel Proteins in the Rat Ovary.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026

Same journal

Novel Hybrid Semiconductor-Cellular Standard for 3D FIB-SEM Nanotomography Analysis.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026

Same journal

Morpho-Anatomical Characterization of the Raisin Tree, Hovenia dulcis Thunb., Utilizing Optical Light and Scanning Electron Microscopy With Energy Dispersive X-Ray Spectroscopy.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026

Same journal

Comparative Aspects of the Hemipenial Architecture of the Arabian Horned Viper Cerastes gasperettii: Macroscopic and Microscopic Analysis With Evolutionary Perspectives.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Benchmark Tests of Atom Segmentation Deep Learning Models with a Consistent Dataset.

Jingrui Wei¹, Ben Blaiszik^2,3, Aristana Scourtas^2,3

¹Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|September 25, 2023

Summary

This summary is machine-generated.

We developed a benchmark dataset to evaluate neural network (NN) models for locating atomic columns in scanning transmission electron microscopy (STEM) images. Performance varies with image quality and data outside the training set.

Keywords:

atomic scale image segmentation neural networks scanning transmission electron microscopy (STEM)

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Jul 15, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Materials Science
Electron Microscopy
Computational Science

Background:

Atomic-resolution scanning transmission electron microscopy (STEM) images contain crucial information, often summarized by atomic column positions.
Neural networks (NNs) offer efficient, automated methods for locating atomic columns in STEM images, leading to numerous models and datasets.

Purpose of the Study:

To establish a benchmark dataset for evaluating NN models used in atomic column localization within STEM images.
To assess the performance of recent NN models on both simulated and experimental STEM data.

Main Methods:

Development of a comprehensive benchmark dataset comprising simulated and experimental STEM images.
Evaluation of two distinct NN models using the benchmark dataset to determine their accuracy in atom location.

Main Results:

Both NN models demonstrated high performance across various image qualities and crystal lattices.
Significant performance disparities were observed based on image quality.
Models exhibited poor performance on images dissimilar to the training data, such as interfaces with high background intensity variations.

Conclusions:

The benchmark dataset and evaluated NN models are valuable resources for the materials science community.
Availability via the Foundry service facilitates dissemination, discovery, and reuse of these machine learning tools.
Further development is needed to improve NN robustness for diverse imaging conditions and data types.