Abstract
The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.
Highlights
The X-ray computed tomography (XCT) is a robust characterization tool in which the battery field has shown tremendous interest during the last decade[1,2,3]
The 3D analysis and electrochemical simulation are usually preceded by a step of semantic segmentation, which consists of digitally partitioning each voxel of the raw stack of tomograms (Fig. 1a left part) into different phases (Fig. 1a right part)
LRCS-Net (Fig. 2) used throughout this work has been optimized to segment efficiently nano-CT images of the battery electrode and is derived from Seg-Net[24] and Xlearn[32] artificial neural networks
Summary
The X-ray computed tomography (XCT) is a robust characterization tool in which the battery field has shown tremendous interest during the last decade[1,2,3] It provides valuable 3D morphological information on the battery materials and electrode architectures. Pietsch et al.[15] has firstly discussed the impact of segmentation on the determination of morphological and transport properties for commercial anode materials They studied each parameter in the XCT image post-processing and the thresholding in segmentation. The use of the thresholding approach (Fig. 1c) or the automatic K-means method (Fig. 1d) applying on a 2D histogram leads to an overestimation of the CBD phase and a coarse separation of the interfaces These as-segmented volumes with the NMC particles are firmly surrounded by the CBD. We will show that the accuracy can be improved by reusing the kernels of a pre-trained network, namely transfer learning
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