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

2024-10-02 18:22:04
Ganesh Raghavendran (Department of NanoEngineering-University of California San Diego), Bing Han (Department of NanoEngineering-University of California San Diego), Fortune Adekogbe (Department of Chemical and Petroleum Engineering-University of Lagos), Shuang Bai (Department of NanoEngineering-University of California San Diego), Bingyu Lu (Department of NanoEngineering-University of California San Diego), William Wu (Del Norte High School), Minghao Zhang (Department of NanoEngineering-University of California San Diego), Ying Shirley Meng (Department of NanoEngineering-University of California San Diego, Pritzker School of Molecular Engineering-University of Chicago)

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.

Abstract (translated)

在电池研究领域,处理高分辨率显微镜图像的处理是一个具有挑战性的任务,因为它涉及到处理复杂的图像,并需要对参与其中的组件有 prior 理解。近年来,利用深度学习方法对图像进行分析已经引起了相当大的兴趣,多个研究在电池研究领域应用了这种技术进行图像分割和分析。然而,对复合材料中检测相和组件的自动化分析仍然是一个未被探索的领域。本文提出了一种从原始高分辨率透射电子显微镜(TEM)图像中检测组件和相分割的新工作流程,使用训练好的 U-Net 分割模型。所开发的分割模型可以加速组件和相的检测,降低对观察大量TEM图像的时间和认知要求,从而减轻了人为错误的风险。这种方法提出了一种新颖且有效的图像分析方法,具有广泛的应用前景,不仅仅局限于电池领域,还适用于其他由相和组成分布特点决定的领域,如合金生产。

URL

https://arxiv.org/abs/2410.01928

PDF

https://arxiv.org/pdf/2410.01928.pdf


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