Lithium metal battery (LMB) has the potential to be the next-generation battery system because of their high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinder the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use computerized X-ray tomography (XCT) imaging to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new binary semantic segmentation approach using a transformer-based neural network (T-Net) model capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed T-Net with three other algorithms, such as U-Net, Y-Net, and E-Net, consisting of an Ensemble Network model for XCT analysis. Our results show the advantages of using T-Net in terms of object metrics, such as mean Intersection over Union (mIoU) and mean Dice Similarity Coefficient (mDSC) as well as qualitatively through several comparative visualizations.

翻译：金属锂电池(LMB)由于其高理论能量密度高,有可能成为下一代电池系统(LMB),但是,称为丁酸的缺陷是由异质锂(Li)电镀形成的,这阻碍了LMB的开发和利用。观测脱义的形态学的非破坏性技术经常使用计算机化X射线透视成像(XCT)来提供交叉视图。要检索电池内三维结构,图像分割对于定量分析XCT图像至关重要。这项工作提议采用一种新的二元语解分解方法,使用基于变异器的神经网络(T-Net)模型来将脱义体分离出XCT数据。此外,我们将拟议的T-Net的性能与其他三种算法,例如U-Net、Y-Net和E-Net的性算法进行比较,这些算法由用于XCT分析的共聚网络模型组成。我们的结果显示,使用T-Net在物体指标方面的优势,例如,以中位的互交式对齐(MIU)和以中等的可视化方式对等的可视化技术。