Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network

Liu, Xin; Li, Boyi; Liu, Chengcheng; Ta, Dean

doi:10.1007/s43657-023-00094-1

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…During the experiments, subjects were asked not to move their head. A Polysomnography (PSG) device with a sampling frequency of 256 Hz was applied for EOG data acquisition [24] .…”

Section: Methodsmentioning

confidence: 99%

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

Zeng,

Tao,

Zhu

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson’s disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. Objective: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. Methods and procedures: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. Results: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from −50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. Conclusion: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson’s disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.

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“…During the experiments, subjects were asked not to move their head. A Polysomnography (PSG) device with a sampling frequency of 256 Hz was applied for EOG data acquisition [24] .…”

Section: Methodsmentioning

confidence: 99%

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

Zeng,

Tao,

Zhu

et al. 2024

IEEE J. Transl. Eng. Health Med.

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show abstract

“…However, whether the physiological information implicitly contained in the EEG signals can distinguish the seizure severity of KCNQ2 EE has not been systematically investigated. Neonates spend two-third of their lives sleeping [13], [14]. Thus, the acquisition of sleep EEG is convenient for practical use.…”

Section: Introductionmentioning

confidence: 99%

Automatic and Quantitative Electroencephalographic Characterization of Drug-Resistant Epilepsy in Neonatal KCNQ2 Epileptic Encephalopathy

Zeng

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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KCNQ2 epileptic encephalopathy is relatively common in early-onset neonatal epileptic encephalopathy and seizure severity varied widely, categorized as drug-sensitive epilepsy and drug-resistant epilepsy. However, in clinical practice, anti-seizure medicines need to be gradually adjusted based on seizure control which undoubtedly increases the economic burden of patients, so further positive anti-seizure regimens depend on whether seizure severity can be predicted in advance. In this paper, we proposed a reliable assessment to differentiate between drug-sensitive epilepsy and drug-resistant epilepsy caused by KCNQ2 pathogenic variants. Based on the electroencephalogram (EEG) and electrooculogram (EOG) signals, twenty-four classical temporal and spectral domain features were extracted and Gradient BoostingDecision Tree (GBDT) was employed to distinguish between patients with drug-sensitive epilepsy and drug-resistant epilepsy. In addition, we also systematically investigated the impact of channel combination and feature combi-Manuscript

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Trustworthy in silico cell labeling via ensemble-based image translation

Imboden,

Liu,

Payne

et al. 2023

Biophysical Reports

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Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network

Cited by 4 publications

References 40 publications

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

Automatic and Quantitative Electroencephalographic Characterization of Drug-Resistant Epilepsy in Neonatal KCNQ2 Epileptic Encephalopathy

Trustworthy in silico cell labeling via ensemble-based image translation

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