Quenum Jerome, Zenyuk Iryna V, Ushizima Daniela
Department of Electrical Engineering and Computer Science, Berkeley College of Engineering, University of California, Berkeley, CA 94720, USA.
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
J Imaging. 2023 May 31;9(6):111. doi: 10.3390/jimaging9060111.
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) 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 semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations.
锂金属电池（LMB）因其高理论能量密度而有潜力成为下一代电池系统。然而，由异质锂（Li）电镀形成的称为枝晶的缺陷阻碍了LMB的开发和利用。观察枝晶形态的非破坏性技术通常使用X射线计算机断层扫描（XCT）来提供横截面视图。为了获取电池内部的三维结构，图像分割对于定量分析XCT图像变得至关重要。这项工作提出了一种新的语义分割方法，使用一种基于Transformer的神经网络TransforCNN，它能够从XCT数据中分割出枝晶。此外，我们将所提出的TransforCNN与其他三种算法U-Net、Y-Net和E-Net的性能进行比较，这三种算法组成了一个用于XCT分析的集成网络模型。我们的结果表明，在评估过分割指标时，如平均交并比（mIoU）和平均Dice相似系数（mDSC），以及通过几个定性比较可视化时，使用TransforCNN具有优势。
