Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Alternative to the statistical mass confusion of testing for "no effect".

The Journal of cell biology·2025
Same author

Mistargeted retinal axons induce a synaptically independent subcircuit in the visual thalamus of albino mice.

eLife·2025
Same author

Mistargeted retinal axons induce a synaptically independent subcircuit in the visual thalamus of albino mice.

bioRxiv : the preprint server for biology·2024
Same author

Author Correction: Subcellular pathways through VGluT3-expressing mouse amacrine cells provide locally tuned object-motion-selective signals in the retina.

Nature communications·2024
Same author

Subcellular pathways through VGluT3-expressing mouse amacrine cells provide locally tuned object-motion-selective signals in the retina.

Nature communications·2024
Same author

Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework.

BMC research notes·2021

Related Experiment Video

Updated: Jun 27, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K

Evaluating the Quality of Serial EM Sections with Deep Learning.

Mahsa Bank Tavakoli1,2,3, Josh L Morgan1,2,3

  • 1Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, Euclid Ave., St. Louis, MO 63110, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|May 3, 2024
PubMed
Summary

Automated image quality assessment using a modified ResNet-50, termed Quality Evaluation Network (QEN), reliably predicts user scores for serial section scanning electron microscopy (ssSEM) images. This tool helps identify and retake poor-quality images during ssSEM acquisition.

Keywords:
convolutional neural networks (CNNs)deep learningimage quality evaluationserial section scanning electron microscopy (ssSEM)

More Related Videos

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.0K
A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy
09:46

A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy

Published on: January 17, 2018

14.2K

Related Experiment Videos

Last Updated: Jun 27, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.0K
A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy
09:46

A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy

Published on: January 17, 2018

14.2K

Area of Science:

  • Microscopy
  • Image Analysis
  • Machine Learning

Background:

  • Automated image acquisition enhances serial section scanning electron microscopy (ssSEM) throughput.
  • Image quality in ssSEM can fluctuate due to autofocusing and beam stigmation.
  • Automated quality evaluation is crucial for generating high-quality ssSEM datasets.

Purpose of the Study:

  • To develop and validate an automated method for assessing ssSEM image quality.
  • To determine if convolutional neural networks can replicate human quality evaluations.
  • To enable real-time identification of imaging issues during ssSEM acquisition.

Main Methods:

  • Tested multiple convolutional neural networks for ssSEM image quality evaluation.
  • Developed a modified ResNet-50, named Quality Evaluation Network (QEN).
  • Trained and validated QEN against user-generated quality scores.

Main Results:

  • QEN reliably predicted user-generated quality scores for ssSEM images.
  • The Quality Evaluation Network demonstrated high accuracy in assessing image quality.
  • QEN can be run in parallel with ssSEM acquisition for immediate feedback.

Conclusions:

  • A modified ResNet-50 (QEN) provides reliable automated quality assessment for ssSEM images.
  • QEN facilitates rapid identification of imaging problems, enabling image retakes.
  • Publicly shared code and dataset support the use and further development of QEN.