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

## TL;DR

This paper introduces a deep learning-based image segmentation method for atom-probe tomography data, enabling automatic, scalable, and accurate phase interface detection without manual labeling, improving microstructural analysis.

## Contribution

The authors develop a novel deep neural network approach for phase segmentation in APT data, transferring knowledge from natural images and eliminating the need for interface labeling.

## Key findings

- Accurately segments APT data into different phases.
- Provides consistent interface detection across various geometries.
- Outperforms traditional iso-concentration methods in visualization and quantitative analysis.

## 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 propose 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.