  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, 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 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.
