Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection

Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for...

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Autores principales:	Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler, Subramanian K. R. S. Sankaranarayanan, David N. Seidman, Prasanna Balaprakash, Olle Heinonen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/44bbc6b308d1471da03b8e1a1ab3c668
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spelling	oai:doaj.org-article:44bbc6b308d1471da03b8e1a1ab3c6682021-12-02T13:56:50ZPhase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection10.1038/s41598-019-56649-82045-2322https://doaj.org/article/44bbc6b308d1471da03b8e1a1ab3c6682019-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-56649-8https://doaj.org/toc/2045-2322Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.Sandeep MadireddyDing-Wen ChungTroy LoefflerSubramanian K. R. S. SankaranarayananDavid N. SeidmanPrasanna BalaprakashOlle HeinonenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sandeep Madireddy Ding-Wen Chung Troy Loeffler Subramanian K. R. S. Sankaranarayanan David N. Seidman Prasanna Balaprakash Olle Heinonen Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
description	Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.
format	article
author	Sandeep Madireddy Ding-Wen Chung Troy Loeffler Subramanian K. R. S. Sankaranarayanan David N. Seidman Prasanna Balaprakash Olle Heinonen
author_facet	Sandeep Madireddy Ding-Wen Chung Troy Loeffler Subramanian K. R. S. Sankaranarayanan David N. Seidman Prasanna Balaprakash Olle Heinonen
author_sort	Sandeep Madireddy
title	Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
title_short	Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
title_full	Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
title_fullStr	Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
title_full_unstemmed	Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
title_sort	phase segmentation in atom-probe tomography using deep learning-based edge detection
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/44bbc6b308d1471da03b8e1a1ab3c668
work_keys_str_mv	AT sandeepmadireddy phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection AT dingwenchung phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection AT troyloeffler phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection AT subramaniankrssankaranarayanan phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection AT davidnseidman phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection AT prasannabalaprakash phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection AT olleheinonen phasesegmentationinatomprobetomographyusingdeeplearningbasededgedetection
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Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection

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