Segmentation of tomography datasets using 3D convolutional neural networks

James, Jim; Pruyne, Nathan; Stan, Tiberiu; Schwarting, Marcus; Yeom, Jiwon; Hong, Seungbum; Voorhees, Peter W.; Blaiszik, Ben; Foster, Ian

doi:10.1016/j.commatsci.2022.111847

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…The following set of hyperparameters was chosen due to their reported contribution to the model performance:• Learning rate (LR): This hyperparameter defines the amount by which the model weights are updated during backpropagation and control the speed of the learning process; it is recognised as one of the hyperparameters having the greatest impact on the model performance. 3,65 Considering the recommendations in the literature 3,66,67 and the range of values used in the studies presented in Table 1, three learning rates were evaluated: 10 −2 , 10 −3 , and 10 −4 .• Learning rate reduction factor: As the training progresses, the model may benefit from reducing the learning rate in order to find further optimal states that would have been overlooked by larger learning rates. 19 To account for this observation, the learning rate was automatically reduced by a user-defined factor if the control set loss did not improve during five consecutive epochs.…”

Section: Methodsmentioning

confidence: 99%

“…• Learning rate (LR): This hyperparameter defines the amount by which the model weights are updated during backpropagation and control the speed of the learning process; it is recognised as one of the hyperparameters having the greatest impact on the model performance. 3,65 Considering the recommendations in the literature 3,66,67 and the range of values used in the studies presented in Table 1, three learning rates were evaluated: 10 À2 , 10 À3 , and 10 À4 . • Learning rate reduction factor: As the training progresses, the model may benefit from reducing the learning rate in order to find further optimal states that would have been overlooked by larger learning rates.…”

Section: Cnn Hyperparameters Selectionmentioning

confidence: 99%

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The effect of convolutional neural network architectures on phase segmentation of composite material X-ray micrographs

Galvez-Hernandez

Kratz

2023

Journal of Composite Materials

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Porosity severely reduces the mechanical performance of composite laminates and methods for automatic segmentation of void phases are growing. This study investigates porosity in composite materials that take the form of interlaminar voids and dry tow areas. Deep Learning was used for the segmentation of X-ray micrographs via the implementation of eight state-of-the-art Convolutional Neural Network (CNN) architectures trained with data sets containing twenty-five, fifty, and one-hundred images. The combination of hyperparameters providing the highest accuracy for each architecture and training set size was achieved through the optimisation of six relevant hyperparameters, including the cut-off probability applied to output probability maps. Additionally, the properties of the CNN architectures ( e.g., layer typology, connections, density…) were found to play a determining role, not only in the segmentation results but also in the associated computing effort. U-Net and FCDenseNet outperformed the FCN-8s, FCN-16, SegNet, LinkNet, ResNet18 and Xception CNN architectures. However, the CNNs generally outperformed the standard thresholding approaches, especially in sub-volumes containing low porosity (1.07%) where the influence on strength is very sensitive in high-performance composites. In low porosity samples, U-Net and FCDenseNet consistently segmented voids to 85% + accuracy, whereas thresholding was only half as accurate, at around 40%. The results provide a strong motivation to replace thresholding as a segmentation method for composite X-ray micrographs. In terms of efficiency, the reduced complexity of the U-Net network allowed for an average reduction of the training time (−36%) and prediction time (−17%) when compared to FCDenseNet.

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Section: Methodsmentioning

confidence: 99%

Section: Cnn Hyperparameters Selectionmentioning

confidence: 99%

The effect of convolutional neural network architectures on phase segmentation of composite material X-ray micrographs

Galvez-Hernandez

Kratz

2023

Journal of Composite Materials

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“…Like all such methods, it is trained on a small amount of labeled (i.e., pre-segmented) data and, once trained, can produce segmentation predictions. Deep learning architectures have also been extended to 3D and applied in medical and materials science fields, which fulfill the need to segment variable 3D structures [13][14][15][16][17] . Much effort [18][19][20][21][22] has been dedicated to exploiting machine learning to accelerate the analysis of CT data of batteries, most of which are focused on the electrodes, especially the cathodes.…”

Section: Introductionmentioning

confidence: 99%

Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

Ying

Perlmutter

et al. 2023

npj Comput Mater

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Operando X-ray micro-computed tomography (µCT) provides an opportunity to observe the evolution of Li structures inside pouch cells. Segmentation is an essential step to quantitatively analyzing µCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the datasets. Herein, we report a computational approach, batteryNET, to train an Iterative Residual U-Net-based network to detect Li structures. The resulting semantic segmentation shows singular Li-related component changes, addressing diverse morphologies in the dataset. In addition, visualizations of the dead Li are provided, including calculations about the volume and effective thickness of electrodes, deposited Li, and redeposited Li. We also report discoveries about the spatial relationships between these components. The approach focuses on a method for analyzing battery performance, which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

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Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

Hirabayashi,

Iga,

Ogawa

et al. 2024

npj Comput Mater

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The microstructure is a critical factor governing the functionality of ceramic materials. Meanwhile, microstructural analysis of electron microscopy images of polycrystalline ceramics, which are geometrically complex and composed of countless crystal grains with porosity and secondary phases, has generally been performed manually by human experts. Objective pixel-based analysis (semantic segmentation) with high accuracy is a simple but critical step for quantifying microstructures. In this study, we apply neural network-based semantic segmentation to secondary electron images of polycrystalline ceramics obtained by three-dimensional (3D) imaging. The deep-learning-based models (e.g., fully convolutional network and U-Net) by employing a dataset based on a 3D scanning electron microscopy with a focused ion beam is found to be able to recognize defect structures characteristic of polycrystalline materials in some cases due to artifacts in electron microscopy imaging. Owing to the training images with improved depth accuracy, the accuracy evaluation function, intersection over union (IoU) values, reaches 94.6% for U-Net. These IoU values are among the highest for complex ceramics, where the 3D spatial distribution of phases is difficult to locate from a 2D image. Moreover, we employ the learned model to successfully reconstruct a 3D microstructure consisting of giga-scale voxel data in a few minutes. The resolution of a single voxel is 20 nm, which is higher than that obtained using a typical X-ray computed tomography. These results suggest that deep learning with datasets that learn depth information is essential in 3D microstructural quantifying polycrystalline ceramic materials. Additionally, developing improved segmentation models and datasets will pave the way for data assimilation into operando analysis and numerical simulations of in situ microstructures obtained experimentally and for application to process informatics.

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Segmentation of tomography datasets using 3D convolutional neural networks

Cited by 3 publications

References 30 publications

The effect of convolutional neural network architectures on phase segmentation of composite material X-ray micrographs

The effect of convolutional neural network architectures on phase segmentation of composite material X-ray micrographs

Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

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