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

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

5.1K
A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Overcoming Impediments to the Qualification of Additively Manufactured Polymer Components: The Case of ULTEM.

Polymers·2026
Same author

An Analysis of Notch Toughness of Electron Beam Powder Bed Fused (EB-PBF) Ti-6Al-4V in Relation to Build Orientation and Mechanical Properties.

Materials (Basel, Switzerland)·2026
Same author

Stoichiometric Measurement of Hydroxyapatite by Atom Probe Tomography: Effects of UV and Deep UV Laser-assisted Analytical Conditions.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2025
Same author

Removing Alpha Case from Laser Powder Bed Fusion Components by Cavitation Abrasive Surface Finishing.

Materials (Basel, Switzerland)·2025
Same author

Stratification of fluoride uptake among enamel crystals with age elucidated by atom probe tomography.

Communications materials·2024
Same author

Durability of Ultem 9085 in Marine Environments: A Consideration in Fused Filament Fabrication of Structural Components.

Polymers·2024
Same journal

Magnetic Anisotropy Dominates over Physical and Magnetic Structure in Performance of Magnetic Nanoflowers.

Small structures·2026
Same journal

Quantizing DNA Metallization For Site-Defined Growth Of Single Quantum Emitters.

Small structures·2026
Same journal

Enoki-Inspired Microfibers and Extracellular Matrix Enhance Biaxially Interlocking Interfaces.

Small structures·2024
Same journal

Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure.

Small structures·2024
Same journal

Vascularized Hepatocellular Carcinoma on a Chip to Control Chemoresistance through Cirrhosis, Inflammation and Metabolic Activity.

Small structures·2023
Same journal

A Colorimetric Test to Differentiate Patients Infected with Influenza from COVID-19.

Small structures·2021
See all related articles

Related Experiment Video

Updated: Apr 28, 2026

Sampling and Pretreatment of Tooth Enamel Carbonate for Stable Carbon and Oxygen Isotope Analysis
07:57

Sampling and Pretreatment of Tooth Enamel Carbonate for Stable Carbon and Oxygen Isotope Analysis

Published on: August 15, 2018

14.0K

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy

Carli Marsico1,2, Cameron Renteria1,3, Jack R Grimm1

  • 1Department of Materials Science and Engineering, University of Washington, Box 352120, Seattle 98195, WA, USA.

Small Structures
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning method accurately analyzes dental enamel microstructure. This technique quantifies enamel rod decussation, aiding the development of advanced structural materials inspired by tooth resilience.

Keywords:
bioinspirationsenamelimage analysesmachine learningmachine visionsscanning electron microscopies

More Related Videos

Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM
10:06

Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM

Published on: July 10, 2014

15.0K
Author Spotlight: Establishing an Accurate Microhardness Testing Protocol for Craniofacial Tissues
06:16

Author Spotlight: Establishing an Accurate Microhardness Testing Protocol for Craniofacial Tissues

Published on: April 26, 2024

1.5K

Related Experiment Videos

Last Updated: Apr 28, 2026

Sampling and Pretreatment of Tooth Enamel Carbonate for Stable Carbon and Oxygen Isotope Analysis
07:57

Sampling and Pretreatment of Tooth Enamel Carbonate for Stable Carbon and Oxygen Isotope Analysis

Published on: August 15, 2018

14.0K
Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM
10:06

Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM

Published on: July 10, 2014

15.0K
Author Spotlight: Establishing an Accurate Microhardness Testing Protocol for Craniofacial Tissues
06:16

Author Spotlight: Establishing an Accurate Microhardness Testing Protocol for Craniofacial Tissues

Published on: April 26, 2024

1.5K

Area of Science:

  • Biomaterials Science
  • Materials Engineering
  • Microscopy and Imaging

Background:

  • Dental enamel, the hard outer layer of teeth, exhibits exceptional strength and fracture resistance.
  • This durability is largely due to the unique decussated arrangement of enamel rods, its fundamental microstructure.
  • Enamel's microstructure inspires the design of novel, high-performance structural materials.

Purpose of the Study:

  • To develop and validate a machine learning-based segmentation method for quantitative analysis of dental enamel microstructure.
  • To overcome limitations in traditional imaging and analysis techniques for complex microstructures like decussated enamel rods.
  • To enable precise calculation of microstructural parameters in mammalian tooth enamel.

Main Methods:

  • Utilized scanning electron microscopy (SEM) for image acquisition of tooth enamel.
  • Applied a machine learning segmentation approach, employing a pretrained convolutional neural network (CNN) to augment training data.
  • Trained a random forest classifier using a minimal dataset (n=3 images) for effective image segmentation.
  • Validated the segmentation method and applied it to analyze enamel microstructural parameters across different mammalian species.

Main Results:

  • Successfully segmented SEM images of dental enamel using a machine learning approach with a small training set.
  • Quantified key microstructural parameters related to enamel rod decussation.
  • Demonstrated the method's efficacy and applicability to enamel samples from various mammalian species.
  • Validated the accuracy and reliability of the developed segmentation technique.

Conclusions:

  • The developed machine learning segmentation method provides a robust and efficient tool for quantitative analysis of dental enamel microstructure.
  • This approach overcomes previous imaging and processing challenges, enabling detailed microstructural characterization.
  • The methodology is transferable and applicable to the analysis of other biological hard tissues, advancing materials science and biomimicry.