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

AI Article Synopsis

  • Atom-probe tomography (APT) is a technique for analyzing microstructural features at the nano and atomic scale, especially useful for studying interfaces in materials with different phases.
  • Traditional methods for identifying interfaces in APT data involve subjective, manual processes that can lead to inconsistencies, particularly due to local variations in composition.
  • The text introduces a new method using deep neural networks for automatic segmentation of APT data, which efficiently identifies phases without needing expensive labeling and demonstrates accuracy through visual and quantitative comparisons to traditional methods.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934719PMC
http://dx.doi.org/10.1038/s41598-019-56649-8DOI Listing

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