A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images
[3];
[6]
- Department of Materials Science and Engineering University of Washington Box 352120 Seattle 98195 WA USA, Materials Science and Engineering Department Idaho National Laboratory Idaho Falls 98195 ID USA
- Department of Materials Science and Engineering University of Washington Box 352120 Seattle 98195 WA USA, Department of Oral Health Sciences School of Dentistry University of Washington Seattle 98195 WA USA
- Department of Materials Science and Engineering University of Washington Box 352120 Seattle 98195 WA USA
- Science and Humanities Faculty Institución Universitaria Digital de Antioquia Medellín 050010 Colombia
- Materials Science and Engineering Department Idaho National Laboratory Idaho Falls 98195 ID USA
- Department of Materials Science and Engineering University of Washington Box 352120 Seattle 98195 WA USA, Department of Oral Health Sciences School of Dentistry University of Washington Seattle 98195 WA USA, Department of Mechanical Engineering University of Washington Seattle 98195 WA USA, Department of Restorative Dentistry School of Dentistry University of Washington Seattle 98195 WA USA
Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.
- Research Organization:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC07-05ID14517
- OSTI ID:
- 2483924
- Report Number(s):
- INL/JOU--24-80735-Rev000; 2400510
- Journal Information:
- Small Structures, Journal Name: Small Structures Journal Issue: 5 Vol. 6; ISSN 2688-4062
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Germany
- Language:
- English
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