Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

Liu, Zhe; Bagnaninchi, Pierre O.; Yang, Yunjie

doi:10.1109/tmi.2021.3129739

Cited by 20 publications

(20 citation statements)

References 53 publications

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“…In addition, Li et al combined CT with EIT through Cross Gradient regularization [25] and Liang et al integrated ultrasound image into EIT [26] [27]. Apart from these model-based image reconstruction algorithms, Liu et al presented a pioneering study on learning-based dual-modal EIT imaging [28]. These multi-modal methods show the potential of improving EIT image quality by introducing auxiliary imaging information.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

et al. 2023

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The intrinsic issue of low spatial resolution of Electrical Impedance Tomography (EIT) is a long-standing challenge that hinders the capability of performing quantitative analysis based on EIT image. Our recent work demonstrates an impedance-optical dual-modal imaging framework and a deep learning model named Multi-Scale Feature Cross Fusion Network (MSFCF-Net) to realize information fusion and high-quality EIT image reconstruction. However, this framework's performance is limited by the accuracy of the mask image obtained from an auxiliary imaging modality. This paper further proposes a two-stage deep neural network, which is the enhanced version of the MSFCF-Net (named En-MSFCF-Net), to automatically improve mask image and conduct information fusion and image reconstruction. Compared to MSFCF-Net, En-MSFCF-Net demonstrates the superior ability to correct the inaccurate mask image, leading to a more accurate conductivity estimation. Furthermore, the En-MSFCF-Net also maintains the best shape preservation and conductivity prediction accuracy among given learning-based and modelbased algorithms. Both qualitative and quantitative results indicate that En-MSFCF-Net could make dual-modal imaging more robust in real-world situations.

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Section: Introductionmentioning

confidence: 99%

“…In our previous work [28], an impedance-optical dualmodal imaging framework (see Fig. 1) was proposed and demonstrated noticeable improvements in the quality of EIT images.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

et al. 2023

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“…We propose to constrain the spatial correlation by binary masks available to optimize the structural information. The binary masks could be extracted from, for instance, CT scans [28] and microscopic images in the same ROI using a multi-modal imaging setup [29]. Thereafter, a Mask-guided STGNN network (M-STGNN) (see Fig.…”

Section: B Mask-guided Spatial-temporal Graph Neural Networkmentioning

confidence: 99%

“…Moreover, we introduce external geometric structures from the auxiliary imaging modality in the format of binary masks to further help optimize the structural information. The geometric structure could be obtained from different imaging modalities, such as CT scans [28] and microscopic images [29]. The main contributions of this work are as follows:…”

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confidence: 99%

Mask-Guided Spatial–Temporal Graph Neural Network for Multifrequency Electrical Impedance Tomography

Chen

Liu

et al. 2022

IEEE Trans. Instrum. Meas.

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Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging biomedical imaging modality that exploits frequency-dependent electrical properties. The mfEITimage-reconstruction problem for cell imaging is particularly challenging due to weak signals from miniaturized sensors and high sensitivity to modelling errors. Existing approaches are primarily based on the linearized model and few are applied to the miniaturized setup. Here we report a Mask-guided Spatial-Temporal Graph Neural Network (M-STGNN) to reconstruct mfEIT images in cell culture imaging. The M-STGNN captures simultaneously spatial and frequency correlations, and the spatial correlation is further constrained by geometric structures from auxiliary binary masks, such as CT or microscopic images. We validate the mfEIT approach through numerical simulations and experiments on MCF-7 human breast cancer cell aggregates. The results demonstrate the superiority of M-STGNN over the state of the art with an improvement of approximately 10.7% under the experimental setup. It can be readily extended to multi-modal biomedical imaging applications.

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“…In this paper, inspired by [29], which introduced attention module in skip connection part of UNet network and [30], which proposed attention mechanism for EIT cell image reconstruction, a soft measure data-driven deep learning model: Attention UNet Fully Connected (AU-FC) model is proposed to directly predict the volume fraction of oil-water two-phase flow from the measurement voltages. The network uses the attention mechanism to strengthen the reconstructed multiscale voltages and accurately predicts the oil volume fraction through the fully connected layers.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Voltage Reconstruction With Attention-Based Network for Volume Fraction Prediction of Industrial Oil–Water Two-Phase Flow by EIT

Zhang

Gao

et al. 2022

IEEE Trans. Instrum. Meas.

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Oil-water two-phase flow as a typical twophase flow type widely exists in various industrial processes and the accurate measurement of oil volume fraction plays a significant role in transporting and separating oil-water mixture in the processes. Electrical Impedance Tomography (EIT) as a merging technology with the advantages of non-invasive, low cost and real-time measurement is widely applied in the industrial field to measure the volume fraction for different types of twophase flows. However, the measurement process of taking homogeneous reference voltages is time-consuming and costly. To cope with the problem, in the paper, by establishing an end-to-end mapping between measurement voltages and volume fraction, we propose an Attention UNet-Fully Connected (AU-FC) architecture. Relying on the attention mechanism, the reconstructed voltages having a strong correlation or a weak correlation with volume fraction is highlighted or suppressed respectively. Oil-water two-phase flow experiment was conducted in the NEL facility to collect EIT voltage data. Compared with six stateof-the-art and existing machine learning methods, the proposed method performs better in predicting volume fraction. The results indicate that the proposed AU-FC architecture can accurately and real-time predict the volume fraction of oil-water two-phase flow, which improves the application potential of EIT combined with deep learning method in the industrial field.

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Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

Cited by 20 publications

References 53 publications

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

Mask-Guided Spatial–Temporal Graph Neural Network for Multifrequency Electrical Impedance Tomography

Multiscale Voltage Reconstruction With Attention-Based Network for Volume Fraction Prediction of Industrial Oil–Water Two-Phase Flow by EIT

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