Intracerebral Hemorrhage Imaging Based on Hybrid Deep Learning With Electrical Impedance Tomography

Shi, Yanyan; Wu, Yuehui; Wang, Meng; Tian, Zhiwei; Fu, Feng

doi:10.1109/tim.2023.3284936

Cited by 10 publications

(5 citation statements)

References 48 publications

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“…In the last decade, the evolution of rapid machine learning has contributed to the development of various artificial neural network-based approaches for EIT image reconstruction. They advanced from simple multilayer fully connected networks such as EIT-4LDNN [ 59 ] to complex hybrid convolutional networks [ 40 ]. The cGAN scheme, in the last few years, has become a widely adopted approach for training convolutional networks for EIT image reconstruction.…”

Section: Discussionmentioning

confidence: 99%

“…It is possible to use various ML-based algorithms in EIT image reconstruction, such as artificial neural networks, Random Forests, K-Nearest Neighbors, Elastic Net, Ada Boost, and Gradient Boosting [ 38 ]. In our work, we focused on ANN approaches as they show very promising results in the reconstruction of medical EIT images, for example, the fully connected network [ 39 ] and hybrid convolutional network [ 40 ] for brain EIT or the conditional generative adversarial network (cGAN) for thoracal EIT [ 33 ]. Also, ANN-based classifiers can support stroke differentiation-related decisions [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, according to our knowledge, there have been no attempts to explore its feasibility for human brain CCEIT image reconstruction. Inspired by the study for brain EIT focused on intracerebral hemorrhage imaging [ 40 ], we propose a new approach for more precise brain modeling and apply a cGAN-based algorithm for image reconstruction in human brain CCEIT.…”

Section: Introductionmentioning

confidence: 99%

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Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Ivanenko,

Wanta,

Smolik

et al. 2024

Life

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This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Ivanenko,

Wanta,

Smolik

et al. 2024

Life

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“…As an advanced detection technique, electrical resistive tomography (ERT) [3] has real-time, noninvasive, fast response advantages and is widely utilized in industrial detection [4,5]. The flow velocity computation performed by ERT must depend on the relative algorithms, the most typical of which include both the cross-correlation (CC) [6] principle and convolutional neural networks (CNNs) [7].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the flow velocity can be computed from the crossing time and distance between the two adjacent ERT sensors. However, in most cases, these CC methods remain inaccurate due to the following three problems: unreasonable "frozen" assumption in CC [7], natural ERT limitations [8], and uncertain length of comparing series [9]; however, some progress has been made [10,11].…”

Section: Introductionmentioning

confidence: 99%

Flow Velocity Computation in Solid–Liquid Two-Phase Flow by a Hybrid Network CNN–RKSVM

Li,

Yue,

Liu

2024

Applied Sciences

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As an advanced detection technique, electrical resistive tomography (ERT) has been applied to detect the solid–liquid two-phase flow velocity based on available ERT measurements. The flow velocity computation by ERT must depend on the relative algorithms, including both the cross-correlation (CC) principle and convolutional neural networks (CNNs). However, these two types of algorithms have poor accuracy and generalization under complex measuring conditions and various flow patterns. To address this issue, in this paper, a hybrid network is proposed that combines a CNN with a reproducing kernel-based support vector machine (RKSVM) technique. The features hidden in ERT measurements are extracted using the CNN, and then the flow velocity is computed by the RKSVM in a high-dimensional feature space. According to the ERT measurements in an actual experimental platform, the results show that the hybrid network has higher accuracy and generalization ability for flow velocity computation compared with the existing CC, RKSVM, and CNN methods.

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Modified tuna swarm optimization algorithm for brain stroke imaging with electrical impedance tomography

Lou,

Shi,

Yang

et al. 2024

Engineering Analysis with Boundary Elements

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Intracerebral Hemorrhage Imaging Based on Hybrid Deep Learning With Electrical Impedance Tomography

Cited by 10 publications

References 48 publications

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Flow Velocity Computation in Solid–Liquid Two-Phase Flow by a Hybrid Network CNN–RKSVM

Modified tuna swarm optimization algorithm for brain stroke imaging with electrical impedance tomography

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