Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection.
Sandeep MadireddyDing-Wen ChungTroy LoefflerSubramanian K R S SankaranarayananDavid N SeidmanPrasanna BalaprakashOlle G HeinonenPublished in: Scientific reports (2019)
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.
Keyphrases
- deep learning
- convolutional neural network
- artificial intelligence
- big data
- electronic health record
- neural network
- electron transfer
- machine learning
- molecular dynamics
- ionic liquid
- living cells
- oxidative stress
- electron microscopy
- high resolution
- systematic review
- depressive symptoms
- staphylococcus aureus
- optical coherence tomography
- white matter
- multiple sclerosis
- biofilm formation
- sensitive detection
- loop mediated isothermal amplification