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

Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

You might also read

Related Articles

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

Sort by

Same author

Association of travel nursing with quality outcomes in hospitalized patients.

The American journal of managed care·2026

Same author

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy.

The Review of scientific instruments·2026

Same author

Correlation between in vivo <sup>18</sup>F-flortaucipir PET and whole-brain postmortem histological tau signals in Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Granular Creep and Its Role in Optimizing Solid Electrolyte Fabrication for All-Solid-State Batteries.

Small methods·2026

Same author

copick: An open dataset interface and toolkit for collaborative annotation and analysis of cryo-electron tomography data.

Protein science : a publication of the Protein Society·2026

Same author

Red Blood Cell Exchange Transfusion for Severe Babesiosis.

JAMA internal medicine·2026

Same journal

PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit.

IS&T International Symposium on Electronic Imaging·2025

Same journal

Write Sentence with Images: Revisit the Large Vision Model with Visual Sentence.

IS&T International Symposium on Electronic Imaging·2025

Same journal

mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis.

IS&T International Symposium on Electronic Imaging·2025

Same journal

Scale-up Unlearnable Examples Learning with High-Performance Computing.

IS&T International Symposium on Electronic Imaging·2025

Same journal

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

IS&T International Symposium on Electronic Imaging·2025

Same journal

Egocentric Boundaries on Distinguishing Colliding and Non-Colliding Pedestrians while Walking in a Virtual Environment.

IS&T International Symposium on Electronic Imaging·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Guanhua Hao¹, Eric J Roberts^2,3, Tanny Chavez¹

¹Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720.

IS&T International Symposium on Electronic Imaging

|December 22, 2023

Summary

This summary is machine-generated.

MLExchange is a Machine Learning framework that accelerates scientific image analysis. This platform enables users to train and deploy models for enhanced data processing and segmentation, overcoming traditional limitations.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Jul 1, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Scientific user facilities
Data science
Image processing

Background:

Scientific user facilities generate massive datasets, posing significant image processing challenges.
Real-time analysis and artifact correction require computationally intensive algorithms.
Traditional image segmentation methods struggle with complex, low-contrast scientific data.

Purpose of the Study:

To address challenges in scientific image processing and data analysis.
To accelerate the development and deployment of machine learning models for image segmentation.
To provide an accessible platform for researchers to analyze large experimental and simulation data.

Main Methods:

Developed MLExchange, a Machine Learning framework with interactive web interfaces.
Integrated tools for data upload, visualization, labeling, and network training.
Implemented a web-based application for training, testing, and evaluating machine learning models on tomography data.

Main Results:

MLExchange facilitates interactive training and deployment of machine learning models.
The platform allows for sharing of results and trained models among scientists.
Users can intuitively segment images using various machine learning and deep learning algorithms.

Conclusions:

MLExchange enhances and accelerates scientific data analysis through machine learning.
The framework overcomes limitations of traditional image segmentation, especially for complex datasets.
Interactive web interfaces democratize advanced image analysis for scientific research.