A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images DOI Creative Commons
Carli Marsico,

Cameron Renteria,

Jack Grimm

et al.

Small Structures, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime wear and cyclic contact. To meet this demand, enamel possesses combination high hardness resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely decussation rods, principal unit its microstructure. As such, is inspiring design next‐generation structural materials. However, quantitative descriptions decussated rod microstructure remain limited due challenges encountered in applying computed tomography acquiring quality images appropriate for traditional digital processing methods. Here, machine learning segmentation method applied obtained using scanning electron microscopy support analysis A pretrained convolutional neural network used expand input training image dataset allow random forest classifier, which ultimately segments with very small set ( n = 3 images). validation presented, addition application calculate relevant microstructural parameters tooth from selected species. methodology here equally applicable other hard tissues.

Language: Английский

Phase Contrast Based High Resolution X-Ray Desktop Tomography DOI
Alessandra Maia Marques Martinez Perez, D. Hampai,

A R Di Filippo

et al.

Radiation Physics and Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 112600 - 112600

Published: Feb. 1, 2025

Language: Английский

Citations

0

High throughput automated characterization of enamel microstructure using synchrotron tomography and optical flow imaging DOI
Zherui Guo, Donna Post Guillen, Jack Grimm

et al.

Acta Biomaterialia, Journal Year: 2024, Volume and Issue: 181, P. 263 - 271

Published: April 25, 2024

Language: Английский

Citations

3

Parthenocissus tricuspidata tendril: A mechanically robust structural design with multiple functions DOI
J H Zhou, Lin Zhang, Siyan Zhan

et al.

Journal of the Mechanics and Physics of Solids, Journal Year: 2025, Volume and Issue: unknown, P. 106065 - 106065

Published: Feb. 1, 2025

Language: Английский

Citations

0

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images DOI Creative Commons
Carli Marsico,

Cameron Renteria,

Jack Grimm

et al.

Small Structures, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime wear and cyclic contact. To meet this demand, enamel possesses combination high hardness resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely decussation rods, principal unit its microstructure. As such, is inspiring design next‐generation structural materials. However, quantitative descriptions decussated rod microstructure remain limited due challenges encountered in applying computed tomography acquiring quality images appropriate for traditional digital processing methods. Here, machine learning segmentation method applied obtained using scanning electron microscopy support analysis A pretrained convolutional neural network used expand input training image dataset allow random forest classifier, which ultimately segments with very small set ( n = 3 images). validation presented, addition application calculate relevant microstructural parameters tooth from selected species. methodology here equally applicable other hard tissues.

Language: Английский

Citations

0