3D microCT of lithium metal battery after charge and discharge

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 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. This data record contains a
relevant crop of the original XCT data as well as the corresponding result
of using our proposed transformer-based neural network (T-Net)
for semantic segmentation.