Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France)
MINES ParisTech – PSL Research Univ., Fontainebleau (France)
Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France); Renault Technocentre, Guyancourt (France)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France); Inst. Univ. de France, Paris (France); ALISTORE-European Research Inst. (France)
Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France); ALISTORE-European Research Inst. (France)
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.
@article{osti_1894650,
author = {Su, Zeliang and Decencière, Etienne and Nguyen, Tuan-Tu and El-Amiry, Kaoutar and De Andrade, Vincent and Franco, Alejandro A. and Demortière, Arnaud},
title = {Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images},
annote = {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.},
doi = {10.1038/s41524-022-00709-7},
url = {https://www.osti.gov/biblio/1894650},
journal = {npj Computational Materials},
issn = {ISSN 2057-3960},
number = {1},
volume = {8},
place = {United States},
publisher = {Nature Publishing Group},
year = {2022},
month = {02}}
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part IIIhttps://doi.org/10.1007/978-3-319-24574-4_28