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Application of Deep Learning Workflow for Autonomous Grain Size Analysis

Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterizat...

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Autores principales: Bordas, Alexandre, Zhang, Jingchao, Nino, Juan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369622/
https://www.ncbi.nlm.nih.gov/pubmed/35956777
http://dx.doi.org/10.3390/molecules27154826
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author Bordas, Alexandre
Zhang, Jingchao
Nino, Juan C.
author_facet Bordas, Alexandre
Zhang, Jingchao
Nino, Juan C.
author_sort Bordas, Alexandre
collection PubMed
description Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn’s method) and planimetric (Saltykov’s method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method.
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spelling pubmed-93696222022-08-12 Application of Deep Learning Workflow for Autonomous Grain Size Analysis Bordas, Alexandre Zhang, Jingchao Nino, Juan C. Molecules Article Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn’s method) and planimetric (Saltykov’s method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method. MDPI 2022-07-28 /pmc/articles/PMC9369622/ /pubmed/35956777 http://dx.doi.org/10.3390/molecules27154826 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bordas, Alexandre
Zhang, Jingchao
Nino, Juan C.
Application of Deep Learning Workflow for Autonomous Grain Size Analysis
title Application of Deep Learning Workflow for Autonomous Grain Size Analysis
title_full Application of Deep Learning Workflow for Autonomous Grain Size Analysis
title_fullStr Application of Deep Learning Workflow for Autonomous Grain Size Analysis
title_full_unstemmed Application of Deep Learning Workflow for Autonomous Grain Size Analysis
title_short Application of Deep Learning Workflow for Autonomous Grain Size Analysis
title_sort application of deep learning workflow for autonomous grain size analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369622/
https://www.ncbi.nlm.nih.gov/pubmed/35956777
http://dx.doi.org/10.3390/molecules27154826
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