用扩散模型生成合成电镜图像，自动分析纳米晶粒结构
Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model
- 结合边缘检测与生成扩散模型，自动识别晶粒边界
- 在纳米级晶粒分析中达97.23%平均准确率
- 适合数据稀缺的材料微观结构研究
传统从透射电镜（TEM）和扫描电镜（SEM）图像中提取多晶晶粒结构的方法耗时费力、主观性强，难以实现高通量分析。本文提出一种自动化方法，融合边缘检测与生成式扩散模型，有效识别晶粒、去除噪声并连接断裂边界，符合预测的晶界分布。由于真实样本数据不足，采用七阶段概念导向合成数据生成流程，构建用于训练的合成TEM图像。该方法可推广至其他数据稀缺领域。模型应用于多种金属，可从低分辨率TEM图像重建出与先进实验技术相当的晶粒形貌，平均准确率达97.23%。
原文摘要 · Abstract (English)
The traditional techniques for extracting polycrystalline grain structures from microscopy images, such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM), are labour-intensive, subjective, and time-consuming, limiting their scalability for high-throughput analysis. In this study, we present an automated methodology integrating edge detection with generative diffusion models to effectively identify grains, eliminate noise, and connect broken segments in alignment with predicted grain boundaries. Due to the limited availability of adequate images preventing the training of deep machine learning models, a new seven-stage methodology is employed to generate synthetic TEM images for training. This concept-oriented synthetic data approach can be extended to any field of interest where the scarcity of data is a challenge. The presented model was applied to various metals with average grain sizes down to the nanoscale, producing grain morphologies from low-resolution TEM images that are comparable to those obtained from advanced and demanding experimental techniques with an average accuracy of 97.23%.
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