U²-Net for 3D Electrical Impedance Tomography With Combined Electrodes

Ye, Ming; Zhou, Tong; Li, Xiaocheng; Yang, Lu; Liu, Kai; Yao, Jiafeng

doi:10.1109/jsen.2022.3178119

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…The results showed improvements of 35.5% and 3.74% in the normalized mean square error and Pearson correlation coefficient, respectively. U 2 -Net with combined electrodes was used for 3D Electrical Impedance Tomography [26]. Simulations and experiments indicated that U 2 -Net exhibited better performance than U-Net.…”

Section: Deep Learning Innovations In Electrical Tomographymentioning

confidence: 99%

Rotational Convolution Design in Convolutional Neural Networks for Direct 3D Electromagnetic Tomography

Zhao,

Liu

2024

Applied Sciences

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The three-dimensional (3D) reconstruction of Electromagnetic Tomography (EMT) is an important task for many applications, such as the non-destructive testing of inner defects in rail systems. Additionally, image reconstruction algorithms utilizing deep learning methods have been verified to be useful in recent years. Therefore, the interpretability of deep learning is a question that is relevant to its application in other areas. This paper proposes an innovative rotational convolution pattern, Conv-P, for convolutional neural network (CNN) image reconstruction in a 3D EMT system. This pattern is based on the projection relationships inherent in tomographic imaging, where each convolution is performed on adjacent projections along the excitation rotation direction. The advantage of this pattern is that it can generate the convolution process by utilizing the 3D structural information from real sensors. To verify the effectiveness of this convolution pattern, we constructed a 3D dual-layer 16-coil EMT model and tested its image reconstruction performance. The results demonstrate that, compared with two common convolution patterns, Conv-P achieves a 4.7% and 4.1% increase in the Image Correlation Coefficient (CC), a 19.8% and 13.1% reduction in the Relative Image Error (IE), a 0.67% and 1.59% increase in the Peak Signal-to-Noise Ratio (PSNR), and a 3.24% and 0.74% increase in the Structural Similarity Index Measure (SSIM).

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Section: Deep Learning Innovations In Electrical Tomographymentioning

confidence: 99%

Rotational Convolution Design in Convolutional Neural Networks for Direct 3D Electromagnetic Tomography

Zhao,

Liu

2024

Applied Sciences

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“…Different from the study by ( Chen Z. et al, 2020 ), Ye et al (2021) proposed to expand the data only through the splicing layer and subsequently input it into the U 2 -Net network to realize a hybrid reconstruction method called CAT + U2-Net. In addition, they have also recently proposed a 3D reconstruction method for composite electrode EIT systems using U 2 -Net ( Ye et al, 2022 ).…”

Section: Deep Learning In Eit Image Reconstructionmentioning

confidence: 99%

Advances of deep learning in electrical impedance tomography image reconstruction

Zhang

Tian

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.

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“…Subsequently, Wang et al (2022b) proposed , a high-resolution reconstruction algorithm containing a regularized reconstruction module and a multichannel convolutional network, whose effectiveness has been experimentally demonstrated. Moreover, Ye et al (2023) extended image segmentation methods to 3D EIT reconstruction, using U 2 -Net to solve the 3D EIT inverse problem. While simulation results indicate that this method significantly improves the quality of 3D reconstructions, it has not yet been validated through physical experiments.…”

Section: Introductionmentioning

confidence: 99%

A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks

Zheng,

Li,

Wang

et al. 2024

PeerJ Computer Science

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Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.

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U²-Net for 3D Electrical Impedance Tomography With Combined Electrodes

Cited by 8 publications

References 25 publications

Rotational Convolution Design in Convolutional Neural Networks for Direct 3D Electromagnetic Tomography

Rotational Convolution Design in Convolutional Neural Networks for Direct 3D Electromagnetic Tomography

Advances of deep learning in electrical impedance tomography image reconstruction

A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks

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