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.

Synchrotron-based hard X-ray computed tomography (XCT) for high-resolution. This deposit 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.

Any multi-tif reader, such as Gimp, Preview, ImageJ, among others