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学者姓名：吴水才
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Abstract ：
Fetal electrocardiogram (FECG) signals directly reflect the electrical activity of the fetal heart, enabling the assessment of fetal cardiac health. To effectively separate and extract FECG signals from maternal abdominal electrocardiogram (ECG) signals, this study proposed a W-shaped parallel network, termed Attention R2W-Net, which consisted of two Attention R2U-Nets. In the encoder and decoder, recurrent residual modules were used to replace feedforward convolutional layers, significantly enhancing feature representation and improving noise suppression. Additionally, attention gates were used to replace skip connections, enabling precise correction of low-resolution features using deep features and further improving model performance. The decoders at both ends of the network were utilized to reconstruct FECG and MECG signals, respectively. The algorithm was validated using simulated and real datasets, achieving F1 scores of 99.17%, 98.03%, and 97.08% on the ADFECGDB, PCDB, and NIFECGDB datasets, respectively, demonstrating superior performance in both subjective visual effects and objective evaluation metrics. Attention R2W-Net's ability to extract robustly and accurately FECG signals, even in complex noisy environments, make it a reliable tool for FECG extraction. The proposed method's efficiency and accuracy highlight its potential for widespread clinical application, contributing to improved early diagnosis of fetal cardiac abnormalities.
Keyword ：
recurrent residual convolution recurrent residual convolution Attention R2W-Net Attention R2W-Net fetal electrocardiogram signal extraction fetal electrocardiogram signal extraction
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| GB/T 7714 | Chen, Lin , Wu, Shuicai , Zhou, Zhuhuang . Fetal ECG Signal Extraction from Maternal Abdominal ECG Signals Using Attention R2W-Net [J]. | SENSORS , 2025 , 25 (3) . |
| MLA | Chen, Lin 等. "Fetal ECG Signal Extraction from Maternal Abdominal ECG Signals Using Attention R2W-Net" . | SENSORS 25 . 3 (2025) . |
| APA | Chen, Lin , Wu, Shuicai , Zhou, Zhuhuang . Fetal ECG Signal Extraction from Maternal Abdominal ECG Signals Using Attention R2W-Net . | SENSORS , 2025 , 25 (3) . |
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Abstract ：
The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
Keyword ：
brain age prediction brain age prediction UK biobank UK biobank multimodal neuroimaging multimodal neuroimaging resilient brain aging resilient brain aging accelerated brain aging accelerated brain aging discriminative event-based analysis discriminative event-based analysis
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| GB/T 7714 | Lin, Lan , Wu, Yutong , Liu, Lingyu et al. Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data [J]. | BIOENGINEERING-BASEL , 2024 , 11 (7) . |
| MLA | Lin, Lan et al. "Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data" . | BIOENGINEERING-BASEL 11 . 7 (2024) . |
| APA | Lin, Lan , Wu, Yutong , Liu, Lingyu , Sun, Shen , Wu, Shuicai . Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data . | BIOENGINEERING-BASEL , 2024 , 11 (7) . |
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Abstract ：
Objective: Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors' puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on CT images is designed and implemented. Methods: The system mainly includes three modules: image segmentation and three-dimensional reconstruction, automatic surgical path planning, and image information management. Through organ segmentation and three- dimensional reconstruction based on CT images, the personalized abdominal spatial anatomical structure of patients is obtained, which is convenient for surgical path planning. The weighted summation method based on clinical constraints and the concept of Pareto optimality are used to solve the multi-objective optimization problem, screen the optimal needle entry path, and realize the automatic planning of the thermal ablation path. The image information database was established to store the information related to the surgical path. Results: In the discussion with clinicians, more than 78% of the paths generated by the planning system were considered to be effective, and the efficiency of system path planning is higher than doctors' planning efficiency. Conclusion: After improvement, the system can be used for the planning of the thermal ablation path of a liver tumor and has certain clinical application value.
Keyword ：
surgical planning surgical planning weighted summation weighted summation Pareto optimality Pareto optimality system design system design thermal ablation of tumors thermal ablation of tumors
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| GB/T 7714 | Song, Ziwei , Ding, Feifei , Wu, Weiwei et al. Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images [J]. | SENSORS , 2024 , 24 (11) . |
| MLA | Song, Ziwei et al. "Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images" . | SENSORS 24 . 11 (2024) . |
| APA | Song, Ziwei , Ding, Feifei , Wu, Weiwei , Zhou, Zhuhuang , Wu, Shuicai . Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images . | SENSORS , 2024 , 24 (11) . |
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Abstract ：
The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet's network parameters have a large size. In this paper, we introduced a light pyramid convolution (LPC) block into SonoNet and proposed LPC-SonoNet with reduced network parameters for FUSP detection. The LPC block used pyramid convolution architecture inspired by SimSPPF from YOLOv6 and was able to extract features from various scales with a small parameter size. Using SonoNet64 as the backbone, the proposed network removed one of the convolutional blocks in SonoNet64 and replaced the others with LPC blocks. The proposed LPC-SonoNet model was trained and tested on a publicly available dataset with 12,400 ultrasound images. The dataset with six categories was further divided into nine categories. The images were randomly divided into a training set, a validation set, and a test set in a ratio of 8:1:1. Data augmentation was conducted on the training set to address the data imbalance issue. In the classification of six categories and nine categories, LPC-SonoNet obtained the accuracy of 97.0% and 91.9% on the test set, respectively, slightly higher than the accuracy of 96.60% and 91.70% by SonoNet64. Compared with SonoNet64 with 14.9 million parameters, LPC-SonoNet had a much smaller parameter size (4.3 million). This study pioneered the deep-learning classification of nine categories of FUSPs. The proposed LPC-SonoNet may be used as a lightweight network for FUSP detection.
Keyword ：
fetal ultrasound fetal ultrasound standard plane standard plane convolutional neural network convolutional neural network deep learning deep learning image classification image classification
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| GB/T 7714 | Yu, Tianxiang , Tsui, Po-Hsiang , Leonov, Denis et al. LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection [J]. | SENSORS , 2024 , 24 (23) . |
| MLA | Yu, Tianxiang et al. "LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection" . | SENSORS 24 . 23 (2024) . |
| APA | Yu, Tianxiang , Tsui, Po-Hsiang , Leonov, Denis , Wu, Shuicai , Bin, Guangyu , Zhou, Zhuhuang . LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection . | SENSORS , 2024 , 24 (23) . |
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Abstract ：
This study investigates the relationship between modifiable risk factors and dementia subtypes using data from 460,799 participants in the UK Biobank. Utilizing univariate Cox proportional hazards regression models, we examined the associations between 83 modifiable risk factors and the risks of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Composite scores for different domains were generated by aggregating risk factors associated with ACD, AD, and VD, respectively, and their joint associations were assessed in multivariable Cox models. Additionally, population attributable fractions (PAF) were utilized to estimate the potential impact of eliminating adverse characteristics of the risk domains. Our findings revealed that an unfavorable medical history significantly increased the risk of ACD, AD, and VD (hazard ratios (HR) = 1.88, 95% confidence interval (95% CI): 1.74-2.03, p < 0.001; HR = 1.80, 95% CI: 1.54-2.10, p < 0.001; HR = 2.39, 95% CI: 2.10-2.71, p < 0.001, respectively). Blood markers (PAF = 12.1%; 17.4%) emerged as the most important risk domain for preventing ACD and VD, while psychiatric factors (PAF = 18.3%) were the most important for preventing AD. This study underscores the potential for preventing dementia and its subtypes through targeted interventions for modifiable risk factors. The distinct insights provided by HR and PAF emphasize the importance of considering both the strength of the associations and the population-level impact of dementia prevention strategies. Our research provides valuable guidance for developing effective public health interventions aimed at reducing the burden of dementia, representing a significant advancement in the field.
Keyword ：
risk domains risk domains Cox analysis Cox analysis population attributable fractions population attributable fractions risk factors risk factors dementia dementia
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| GB/T 7714 | Ma, Xiangge , Gao, Hongjian , Wu, Yutong et al. Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank [J]. | BIOMEDICINES , 2024 , 12 (9) . |
| MLA | Ma, Xiangge et al. "Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank" . | BIOMEDICINES 12 . 9 (2024) . |
| APA | Ma, Xiangge , Gao, Hongjian , Wu, Yutong , Zhu, Xinyu , Wu, Shuicai , Lin, Lan . Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank . | BIOMEDICINES , 2024 , 12 (9) . |
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Abstract ：
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.
Keyword ：
deep learning deep learning lifespan brain age lifespan brain age machine learning machine learning neuroimaging neuroimaging brain age prediction brain age prediction
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| GB/T 7714 | Wu, Yutong , Gao, Hongjian , Zhang, Chen et al. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review [J]. | TOMOGRAPHY , 2024 , 10 (8) : 1238-1262 . |
| MLA | Wu, Yutong et al. "Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review" . | TOMOGRAPHY 10 . 8 (2024) : 1238-1262 . |
| APA | Wu, Yutong , Gao, Hongjian , Zhang, Chen , Ma, Xiangge , Zhu, Xinyu , Wu, Shuicai et al. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review . | TOMOGRAPHY , 2024 , 10 (8) , 1238-1262 . |
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Abstract ：
The segmentation of cardiac chambers and the quantification of clinical functional metrics in dynamic echo-cardiography are the keys to the clinical diagnosis of heart disease. Identifying the end-diastolic frames (EDFs) and end-systolic frames (ESFs) and manually segmenting the left ventricle in the echocardiographic cardiac cycle before obtaining the left ventricular ejection fraction (LVEF) is a time-consuming and tedious task for clinicians. In this work, we proposed a deep learning-based fully automated echocardiographic analysis method. We pro-posed a multi-attention efficient feature fusion network (MAEF-Net) to automatically segment the left ventricle. Then, EDFs and ESFs in all cardiac cycles were automatically detected to compute LVEF. The MAEF-Net method used a multi-attention mechanism to guide the network to capture heartbeat features effectively, while sup-pressing noise, and incorporated deep supervision mechanism and spatial pyramid feature fusion to enhance feature extraction capabilities. The proposed method was validated on the public EchoNet-Dynamic dataset (n = 1226). The Dice similarity coefficient (DSC) of the left ventricular segmentation reached (93.10 +/- 2.22)%, and the mean absolute error (MAE) of cardiac phase detection was (2.36 +/- 2.23) frames. The MAE for predicting LVEF was 6.29 %. The proposed method was also validated on a private clinical dataset (n = 22). The DSC of the left ventricular segmentation reached (92.81 +/- 2.85)%, and the MAE of cardiac phase detection was (2.25 +/- 2.27) frames. The MAE for predicting LVEF was 5.91 %, and the Pearson correlation coefficient r reached 0.96. The proposed method may be used as a new method for automatic left ventricular segmentation and quantitative analysis in two-dimensional echocardiography. Our code and trained models will be made available publicly at https://github.com/xiaojinmao-code/MAEF-Net.
Keyword ：
Ejection fraction Ejection fraction Left ventricular segmentation Left ventricular segmentation Cardiac phase detection Cardiac phase detection Deep learning Deep learning Echocardiography Echocardiography
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| GB/T 7714 | Zeng, Yan , Tsui, Po-Hsiang , Pang, Kunjing et al. MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography [J]. | ULTRASONICS , 2023 , 127 . |
| MLA | Zeng, Yan et al. "MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography" . | ULTRASONICS 127 (2023) . |
| APA | Zeng, Yan , Tsui, Po-Hsiang , Pang, Kunjing , Bin, Guangyu , Li, Jiehui , Lv, Ke et al. MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography . | ULTRASONICS , 2023 , 127 . |
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Abstract ：
Lung tumor is the first malignant tumor with the highest mortality, but only no more than one-third of patients can be treated by surgical resection. Microwave ablation (MWA) has become a new adjuvant therapeutic mean for lung tumors because of its low trauma, short treatment time, large ablation volume and wide application range. However, the treatment parameters of MWA, such as input power and ablation time, still depend on the doctors' experience, which leads to the ineffectiveness of MWA. Therefore, the accurate modeling of temperature distribution of lung tumor MWA has become a significant technical problem to be solved. Recent research was devoted to personalized characterization of lung tumor parameters, finite element analysis of temperature distribution in MWA and accurate ablation effect evaluation. In this paper, a review of the recently obtained results and data will be presented and discussed.
Keyword ：
microwave ablation microwave ablation Lung tumor Lung tumor simulation modeling simulation modeling temperature distribution temperature distribution
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| GB/T 7714 | Liu, Ju , Gao, Hongjian , Wang, Jinying et al. Recent research advances on simulation modeling of temperature distribution in microwave ablation of lung tumors [J]. | COMPUTER ASSISTED SURGERY , 2023 , 28 (1) . |
| MLA | Liu, Ju et al. "Recent research advances on simulation modeling of temperature distribution in microwave ablation of lung tumors" . | COMPUTER ASSISTED SURGERY 28 . 1 (2023) . |
| APA | Liu, Ju , Gao, Hongjian , Wang, Jinying , He, Yuezheng , Lu, Xinyi , Cheng, Zhigang et al. Recent research advances on simulation modeling of temperature distribution in microwave ablation of lung tumors . | COMPUTER ASSISTED SURGERY , 2023 , 28 (1) . |
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Abstract ：
Single-antenna microwave ablation (MWA) is mainly used to treat small tumors less than 3 cm in diameter. To obtain a larger coagulation zone in a single ablation, a dual-antenna ablation approach was proposed. A three-dimensional finite element method (FEM) simulation model of parallel dual-antennas was developed. Ex vivo experiments at 50 W for 8 min were performed to verify the model. Both the temperature changes in tissue and the size of the coagulation zone were recorded. The effects of dual-antenna spacing, heating power, and blood perfusion on the coagulation zone were analyzed. Fifteen experiments were carried out. The errors between the mean measurements and simulated results at the set temperature points were 1.08 degrees C, 0.95 degrees C, and 2.1 degrees C, respectively. For the same conditions, the blood perfusion of 1.0, 1.5, and 3.0 kg/(m(3)center dot s) can result in a reduction in the coagulation volume by 18.4%, 25.4%, and 42.5%. As the spacing increased, the coagulation zone of each antenna started to fuse together later and the resulting integral coagulation zone became larger. Dual-antenna MWA is expected to be used for the treatment of tumors larger than 5 cm in diameter.
Keyword ：
temperature distribution temperature distribution tumor ablation tumor ablation finite element method simulation finite element method simulation blood perfusion blood perfusion dual-antenna array dual-antenna array
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| GB/T 7714 | Wang, Jinying , Huang, Shengyang , Gao, Hongjian et al. Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (1) . |
| MLA | Wang, Jinying et al. "Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements" . | APPLIED SCIENCES-BASEL 13 . 1 (2023) . |
| APA | Wang, Jinying , Huang, Shengyang , Gao, Hongjian , Liu, Ju , Zhang, Yubo , Wu, Shuicai . Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements . | APPLIED SCIENCES-BASEL , 2023 , 13 (1) . |
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Abstract ：
It is rare to use the one-stage model without segmentation for the automatic detection of coronary lesions. This study sequentially enrolled 200 patients with significant stenoses and occlusions of the right coronary and categorized their angiography images into two angle views: The CRA (cranial) view of 98 patients with 2453 images and the LAO (left anterior oblique) view of 176 patients with 3338 images. Randomization was performed at the patient level to the training set and test set using a 7:3 ratio. YOLOv5 was adopted as the key model for direct detection. Four types of lesions were studied: Local Stenosis (LS), Diffuse Stenosis (DS), Bifurcation Stenosis (BS), and Chronic Total Occlusion (CTO). At the image level, the precision, recall, mAP@0.1, and mAP@0.5 predicted by the model were 0.64, 0.68, 0.66, and 0.49 in the CRA view and 0.68, 0.73, 0.70, and 0.56 in the LAO view, respectively. At the patient level, the precision, recall, and F1 scores predicted by the model were 0.52, 0.91, and 0.65 in the CRA view and 0.50, 0.94, and 0.64 in the LAO view, respectively. YOLOv5 performed the best for lesions of CTO and LS at both the image level and the patient level. In conclusion, the one-stage model without segmentation as YOLOv5 is feasible to be used in automatic coronary lesion detection, with the most suitable types of lesions as LS and CTO.
Keyword ：
convolutional neural network convolutional neural network coronary artery stenosis detection coronary artery stenosis detection deep learning deep learning coronary angiography coronary angiography without segmentation without segmentation one-stage detection one-stage detection
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| GB/T 7714 | Wu, Hui , Zhao, Jing , Li, Jiehui et al. One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning [J]. | DIAGNOSTICS , 2023 , 13 (18) . |
| MLA | Wu, Hui et al. "One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning" . | DIAGNOSTICS 13 . 18 (2023) . |
| APA | Wu, Hui , Zhao, Jing , Li, Jiehui , Zeng, Yan , Wu, Weiwei , Zhou, Zhuhuang et al. One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning . | DIAGNOSTICS , 2023 , 13 (18) . |
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