用U-Net模型自动分割高分辨电镜图像中的相结构，提升材料分析效率。
Deep learning assisted high resolution microscopy image processing for phase segmentation in functional composite materials
- 基于训练好的U-Net模型处理原始高分辨透射电镜图像。
- 显著降低人工分析时间与认知负担，减少人为误差。
- 适用于电池、合金等多相复合材料的相分布分析。
在电池研究领域，高分辨率显微图像的处理极具挑战性，因图像复杂且需预先了解组分特性。近年来，深度学习在图像分析中备受关注，已有研究用于电池领域的图像分割与分析。然而，针对复合材料中相和组分的高分辨率显微图像自动化分析仍属探索阶段。本文提出一种新工作流，利用训练好的U-Net分割模型从原始高分辨率透射电子显微镜（TEM）图像中检测组分并实现相分割。该模型可加速组分识别与相分割过程，显著降低对大量TEM图像进行人工审阅的时间与认知负荷，从而减少人为错误。该方法为图像分析提供了新颖高效的解决方案，具有广泛适用性，不仅限于电池领域，还可推广至其他具有相与成分分布特征的领域，如合金制造。
原文摘要 · Abstract (English)
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.
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