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 Experiment Video

Updated: Jun 9, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K

Exploiting Temporal Features in Calculating Automated Morphological Properties of Spiky Nanoparticles Using Deep

Muhammad Aasim Rafique1

  • 1Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary

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

Employing automatic content recognition for teaching methodology analysis in classroom videos.

PloS oneยท2022
See all related articles

This study introduces a novel sequential machine learning approach for analyzing nanoparticle growth in electron microscopy images. The method enhances object segmentation and morphological analysis by incorporating temporal dynamics, improving accuracy over traditional spatial methods.

Area of Science:

  • Materials Science
  • Machine Learning
  • Image Analysis

Background:

  • Object segmentation in images typically relies on spatial pixel coherence.
  • Nanoparticle analysis in electron microscopy often uses frame-by-frame segmentation followed by morphological analysis, which is inherently sequential.
  • Temporal regularities in nanoparticle growth processes have been underexplored in segmentation and analysis.

Purpose of the Study:

  • To extend spatially focused morphological analysis by integrating sequential machine learning techniques.
  • To account for temporal relationships in nanoparticle growth.
  • To improve the accuracy and comprehensiveness of nanoparticle morphological property analysis.

Main Methods:

  • Incorporation of hard and soft inductive bias from sequential machine learning.
Keywords:
nanoparticle morphologysemantic segmentationspatiotemporal network

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

475
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.0K

Related Experiment Videos

Last Updated: Jun 9, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

475
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.0K
  • Integration of recurrent layers into a convolutional neural network architecture previously used for nanoparticle analysis.
  • Training the network with a spike-focused loss function for continuous image segmentation.

    • Main Results:

    • The proposed approach successfully captures the sequential growth of gold nanoparticles (Au-SNPs).
    • Continuous segmentation revealed regressive relationships among natural growth features.
    • Generated morphological statistics of nanoparticles demonstrated superiority over earlier methods.

    Conclusions:

    • The fusion of spatial and sequential analysis provides a more robust method for nanoparticle segmentation and morphological characterization.
    • The recurrent neural network architecture effectively models temporal dynamics in nanoparticle evolution.
    • This study sets a new benchmark for analyzing dynamic nanoscale processes using advanced machine learning techniques.