# Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data

arXiv: https://arxiv.org/abs/2001.05022 · HTML: https://mlsanity.com/paper/2001.05022/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis](https://mlsanity.com/paper/2204.04250/) (2022)
- [Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy](https://mlsanity.com/paper/2402.18286/) (2024)
- [SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation](https://mlsanity.com/paper/2404.06638/) (2024)
- [A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM)](https://mlsanity.com/paper/2309.06122/) (2023)
- [Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images](https://mlsanity.com/paper/2203.13875/) (2022)
- [Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives](https://mlsanity.com/paper/2208.04149/) (2022)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [Generalization Across Experimental Parameters in Machine Learning Analysis of High Resolution Transmission Electron Microscopy Datasets](https://mlsanity.com/paper/2306.11853/) (2023)
- [Unsupervised segmentation of irradiationunicodex2010induced orderunicodex2010disorder phase transitions in electron microscopy](https://mlsanity.com/paper/2311.08585/) (2023)
- [Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision](https://mlsanity.com/paper/2411.09896/) (2024)
- [A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles](https://mlsanity.com/paper/2410.01213/) (2024)
- [Big Data, Tiny Targets: An Exploratory Study in Machine Learning-enhanced Detection of Microplastic from Filters](https://mlsanity.com/paper/2510.18089/) (2025)
- [Contrast transfer functions help quantify neural network out-of-distribution generalization in HRTEM](https://mlsanity.com/paper/2512.09067/) (2025)
- [Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2603.16715/) (2026)

## Abstract (arXiv)

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two step pipeline for analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape and defect presence, enabling detection of correlations between these features.