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

结合无监督与监督学习在显微成像中实现完整4H-SiC晶圆的缺陷分析

Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer

本文结合无监督与监督学习及数据挖掘技术，构建自动化图像分析流程，用于从约4万张图像拼接的KOH腐蚀4H-SiC晶圆显微图中提取全部缺陷的类型与位置。

使用随机森林预测电子显微镜中目标检测模型的性能

Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests

本研究提出一种基于随机森林回归的方法，通过提取目标检测模型预测特征，快速估算TEM图像缺陷检测模型的F1分数，在三个数据集上验证了鲁棒性，可辅助评估模型适用性与域偏移情况。

Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs

In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities relevant to informing modeling and understanding of irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful…

Multi-defect microscopy image restoration under limited data conditions

Deep learning methods are becoming widely used for restoration of defects associated with fluorescence microscopy imaging. One of the major challenges in application of such methods is the availability of training data. In this work, we propose a unified method for reconstruction of multi-defect fluorescence microscopy images when training data is limited. Our approach consists of two stages: first, we perform data augmentation using Generative Adversarial Network (GAN) with conditional instance normalization (CIN); second, we train a conditional GAN (cGAN) on paired ground-truth and defected …

Methods to Quantify Dislocation Behavior with Dark-field X-ray Microscopy Timescans of Single-Crystal Aluminum

Crystal defects play a large role in how materials respond to their surroundings, yet there are many uncertainties in how extended defects form, move, and interact deep beneath a material's surface. A newly developed imaging diagnostic, dark-field X-ray microscopy (DFXM) can now visualize the behavior of line defects, known as dislocations, in materials under varying conditions. DFXM images visualize dislocations by imaging the very subtle long-range distortions in the material's crystal lattice, which produce a characteristic adjoined pair of bright and dark regions. Full analysis of how thes…

Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data

We present a fully modular and scalable software pipeline for processing electron microscope (EM) images of brain slices into 3D visualization of individual neurons and demonstrate an end-to-end segmentation of a large EM volume using a supercomputer. Our pipeline scales multiple packages used by the EM community with minimal changes to the original source codes. We tested each step of the pipeline individually, on a workstation, a cluster, and a supercomputer. Furthermore, we can compose workflows from these operations using a Balsam database that can be triggered during the data acquisition …