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

2024-10-02

arXiv.org

AI Summary

High-resolution transmission electron microscopy (TEM) images of functional composites—such as battery electrodes—pose significant challenges for phase segmentation due to low signal-to-noise ratios, multi-scale features, and the labor-intensive, error-prone, and prior-knowledge-dependent nature of manual annotation. Method: We propose the first end-to-end U-Net-based semantic segmentation workflow specifically designed for raw, unenhanced TEM images, eliminating the need for pre-processing or expert-guided labeling. The architecture incorporates domain-specific inductive biases to ensure cross-material generalizability. Contribution/Results: Evaluated on a battery electrode dataset, our model achieves 89.3% mean intersection-over-union (IoU), significantly outperforming human annotators in segmentation consistency. It processes individual images at 40× the speed of expert annotation. This work overcomes a critical bottleneck in automated high-resolution TEM image analysis, establishing a scalable, robust paradigm for quantitative microstructural characterization of advanced materials.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Automated phase segmentation in high-resolution microscopy images.
• Deep learning for efficient component detection in composite materials.
• Reducing human error and time in TEM image analysis.

Innovation

• Uses U-Net model for TEM image segmentation
• Automates phase detection in composite materials
• Reduces time and errors in microscopy analysis