Deep learning assisted high resolution microscopy image processing for phase segmentation in functional composite materials

TL;DR

This paper introduces a deep learning workflow using a U-Net model to automate phase segmentation in high-resolution microscopy images, significantly reducing analysis time and errors in battery research and related fields.

Contribution

It presents a novel deep learning-based method for automated phase detection in TEM images, improving efficiency and accuracy over manual analysis.

Findings

The U-Net model effectively segments phases in TEM images.

Automation reduces analysis time and human error.

Method applicable to other materials analysis domains.

Abstract

In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilization of deep learning methodologies for image analysis has attracted considerable interest in recent years, with multiple investigations employing such techniques for image segmentation and analysis within the realm of battery research. However, the automated analysis of high-resolution microscopy images for detecting phases and components in composite materials is still an underexplored area. This work proposes a novel workflow for detecting components and phase segmentation from raw high resolution transmission electron microscopy (TEM) images using a trained U-Net segmentation model. The developed model can expedite the detection of components and phase segmentation, diminishing the temporal and cognitive demands associated with scrutinizing an extensive array of TEM images, thereby mitigating the potential for human errors. This approach presents a novel and efficient image analysis approach with broad applicability beyond the battery field and holds potential for application in other related domains characterized by phase and composition distribution, such as alloy production.