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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

共 2036 条记录，以下是221-230 订阅

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Accelerating MR Thermometry by Complex Fully Convolutional Network 16

Accelerating MR Thermometry by Complex Fully Convolutional N...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Xu, Sijie Zhao, Yueran Zong, Shenyan Shen, Guofeng Shanghai Jiao Tong University School of Biomedical Engineering Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China

ISBN: (纸本)9798350330755

Real-time temperature monitoring of patients using MRI with the PRF method is essential for ensuring the effectiveness and safety of HIFU treatment. However, the process of acquiring MRI data is slow which makes real-time temperature monitoring seemingly impossible. Deep learning methods have recently emerged in the field of accelerating MRI image reconstruction due to their outstanding performance. However, most existing algorithms disregard the phase information of the complex-valued data in the original MRI k-space, as they predominantly employ real-valued operations, like convolutions. Consequently, this paper introduces a Complex UNet network based on the traditional UNet, incorporating a range of complex-valued operations in the complex domain, such as complex convolutions, complex batch normalization, and complex activation functions. Furthermore, the paper compares its performance in temperature measurement tasks with the ZF method and the Real UNet network that operates in the real-valued domain. Experimental results demonstrate that Complex UNet achieves superior accuracy in reconstructing temperature maps while utilizing considerably fewer parameters than Real UNet. This indicates that complex convolutional networks have the potential to significantly enhance the speed and accuracy of temperature measurement, making them highly suitable for real-time temperature monitoring in clinical applications. © 2023 IEEE.

关键词： Magnetic resonance imaging

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DeepLabV3-FP : a novel deep learning network for hurricane damage assessment of buildings in UAV imagery 16

DeepLabV3-FP : a novel deep learning network for hurricane d...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Han, Dongzhe Yang, Guang Lu, Wangze Xie, Runbo Institute of Disaster Prevention Software Engineering Technology Research Center Sanhe China

ISBN: (纸本)9798350330755

Hurricane disasters are destructive. It seriously threaten people's lives and property safety. It is an important task to assess hurricane damage effectively in emergency rescue mission. With the development of UAV remote sensing technology and deep learning technology, it has become possible to use convolutional neural networks to locate hurricane-affected areas and estimate losses. However, hurricane often cause buildings to collapse, destroy trees and result in a large amount of debris on the ground. The appearance of different ground objects overed with debris may have strong similarities, resulting in low detection accuracy. In this paper, we propose an encoder-decoder network, named DeeplabV3-FP to improve accuracy. The encoder uses ResNet50 with atrous convolution to extract features and output shallow and deep feature maps. The decoder fuses shallow and deep feature maps and restores the original resolution of the image. In the shallow branch, the Dimensionality Reduction Filter (DRF) Module is added to blur out small fragment information. In the deep branch, the Pyramid Pooling Attention (PPA) Module is added to introduce more global context information and enhance important dependencies related to the target. Experimental results on the RescueNet dataset demonstrate that our method has achieved good results and Pixel Accuracy reaches 85.7%. © 2023 IEEE.

关键词： Debris

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Gamma camera design for imaging small organs 16

Gamma camera design for imaging small organs

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Pang, Z. Liu, Y. Jiang, N. Hou, Y. Wei, Q. University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering Beijing100083 China Beijing Novel Medical Equipment Ltd Beijing102206 China

ISBN: (纸本)9798350330755

The invention of γ camera greatly promoted the development of nuclear medicine and provided a more convenient and fast means for the diagnosis of diseases. In order to achieve more accurate results, people are constantly improving the spatial resolution and sensitivity of gamma cameras. Based on CdZnTe (CZT) crystals, a γ camera with coded aperture and CZT detector is designed in this paper. The encoder adopts an modified uniform redundant Array (MURA) mask, the basic mode of the coded aperture is 19×19 array, and the nesting mode of the coded aperture is 37 × 37 MURA array. Gate software was used to conduct Monte Carlo simulation experiments on the coded aperture γ camera system. After obtaining the data, Matlab was used to study the image reconstruction algorithm of γ camera imaging system to detect radioactive sources, and the maximum likelihood expectation maximization (MLEM) method was used to reconstruct the image. By comparing the projection and reconstruction results of different point sources, The sensitivity of the system is 2.95%, the resolution in the X direction is 3.78mm, and the resolution in the Y direction is 1.90mm. © 2023 IEEE.

关键词： Nuclear medicine

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Multi-path Heart Sound Detection Based on Wavelet Scattering and Attention Mechanism 16

Multi-path Heart Sound Detection Based on Wavelet Scattering...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Han, Baokun Chen, Meng Li, Yongjian Liu, Lei Wei, Shoushui Jiang, Xinge School of Control Science and Engineering Shandong University Jinan China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China

ISBN: (纸本)9798350330755

Changes in heart sounds can often be the earliest signs of organic heart disease, and algorithms for the automatic classification of normal and abnormal heart sounds are essential for early diagnosis of cardiovascular disease. This work proposes a multi-path heart sound detection network based on wavelet scattering and attention mechanism algorithms. The wavelet scattering transform, long-short term memory (WST-LSTM) is used to extract the wavelet scattering features of the heart sound signal;the spatial attention network (SAN) is used to extract the spatial domain features of the time-frequency map of the heart sound signal;and the temporal attention network (TAN) is used to extract the time domain features of the time-frequency map. Sixteen features are extracted from each of the three paths, and after combining the features extracted from multiple path networks, a total of 48 fusion features are obtained, the final classification is performed using a support vector machine (SVM). The method proposed in this work has been well validated on the 2016 PhysioNet/CinC Challenge database, with accuracy (Acc), sensitivity (Se), specificity (Sp), and mean accuracy (Macc) of 96.94%, 97.42%, 96.82%, and 97.12%, respectively. This work also investigates the advantages of multi-path learning, discusses the effectiveness of different features and visualises them. The results show that the proposed method can effectively classify both normal and abnormal heart sound samples with good generalisation ability © 2023 IEEE.

关键词： Support vector machines

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Blind Face Restoration via Multi-head Cross-attention and Generative Priors 16

Blind Face Restoration via Multi-head Cross-attention and Ge...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Huo, Zhiyong Hu, Shanlin Nanjing University of Posts and Telecommunications Department of Educational Science and Technology Nanjing China Nanjing University of Posts and Telecommunications Department of Communication and Information Engineering Nanjing China

ISBN: (纸本)9798350330755

In the real world, blind face restoration from face images with unknown degradation is a challenging problem. Directly training deep neural networks often fails to achieve reasonable results due to unknown severe degradations. Existing methods based on generative models produce good results but produce restoration results with overly smooth textures or unnatural overall structures. To address this, we propose a novel approach that combines the strengths of convolutional neural networks and Multi-head Cross-attention. Initially, we employ ResNet50 to generate preliminary W+ latent vectors and multi-resolution scale feature maps. We then enhance the multi-scale feature map using cross attention in a Transformer-based feature match module. This process improves the correlation between spatial features and intermediate semantic features of generativa priors, enhances the distance dependence among different semantic features, and extend latent space to improve semantic expressiveness. We also leverage pre-trained GANs to enhance image reconstruction and introduce an FFT-based frequency-domain loss function to pay more attention to high-frequency features. Experiments show that our model outperforms existing Blind Face Restoration methods in both objective metrics and subjective quality comparisons, especially when restoring severely degraded images in real-world scenarios, resulting in visually realistic results. © 2023 IEEE.

关键词： Restoration

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A Method and System for Power Grid engineering Error Diagnosis Based on Improved Graph Convolutional Networks 16

A Method and System for Power Grid Engineering Error Diagnos...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Zhang, Shoukui Gao, Hongmin Shan, Xin Wu, Haiwei Sun, Shiming Power Dispatch Control Center State Grid Jiangsu Electric Power Co. Ltd Nanjing210018 China Hohai University Information Department Nanjing211100 China Nanjing211106 China

ISBN: (纸本)9798350330755

The integration of power grid operation is more prominent in the era of informatization, which puts higher requirements on real-time dispatching and operation of the power grid. Objectively, it requires strengthening the global analysis and decision-making capabilities of integrated super power grids. Models and real-time data serve as the cornerstone for online analysis and decision-making. The quality of data directly affects the accuracy of online analysis and decision-making results. Massive model data supports more precise integrated analysis results, but also presents huge challenges in model data quality control. To address this issue, we propose an improved graph convolutional network-based method and system for power grid engineering error diagnosis. By adding convolutional layer feature extraction based on traditional graph convolutional networks, we can improve the accuracy of model error diagnosis and reduce training loss. At the same time, we construct a knowledge graph for power grid engineering error diagnosis with nodes and branches as the core. This solves the problem of quality inspection of model data under the comprehensive influence of multiple factors and promotes the transformation of model data from manual maintenance to intelligent maintenance. © 2023 IEEE.

关键词： Decision making

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Suprachoroidal Retinal Stimulation Leveraging Temporal Interference Technology: A Modelling Study 16

Suprachoroidal Retinal Stimulation Leveraging Temporal Inter...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Liu, Jiao Song, Xiaoyu Zhou, Feng Ma, Saidong Liu, Zhengyang Li, Jiaxi Chai, Xinyu Li, Liming Guo, Tianruo Shanghai Jiao Tong University Sjtu School of Biomedical Engineering Shanghai China The University of New South Wales Unsw Graduate School of Biomedical Engineering Sydney Australia

ISBN: (纸本)9798350330755

A suprachoroidal retinal prosthesis is implanted between the choroid and the sclera, offering the advantages of low surgical complexity and high stability. However, the relatively longer distance between electrodes and the retina leads to higher retinal ganglion cells (RGC) excitation threshold and lower spatial resolution. To maximize the advantages of suprachoroidal stimulation (SS), it is of great significance to explore additional strategies to lower the threshold and improve the spatial resolution. Temporal interference (TI) was reported to induce localized neural excitation in deep regions, indicating a promising avenue for enhancing suprachoroidal retinal stimulation. This study combines the advancements of TI and SS, resulting in the novel approach of Temporal Interference-Suprachoroidal Stimulation (TI-SS). The effect of electrode position and size on the distribution of the maximum electric potential (EPmax) generated by TI-SS was investigated by finite element modelling. Higher EPmax values corresponds to lower RGC excitation threshold. In addition, the EPmax distribution induced by TI-SS and SS were compared. Our simulation suggests a decrease in EPmax value when stimulating electrodes are moved from cornea to sclera under TI-SS. And the EPmax distribution exhibits a notable transition from a bimodal distribution to a unimodal distribution as the return electrodes are moved to the posterior of the eyeball and get closer. The EPmax values induced by TI-SS is generally higher than those induced by SS, indicating that RGC excitation threshold triggered by TI-SS is lower compared to SS. This study offers preliminary evidence demonstrating the enhanced performance of TI-SS in comparison to conventional SS approach. © 2023 IEEE.

关键词： Electrodes

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PhysMViT: Non-contact facial video physiological signal measurement network based on multiscale vision transformer 16

PhysMViT: Non-contact facial video physiological signal meas...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Nie, Weiqi Liu, Yang Li, Yulong Liu, Zhi Miao, Danmin Shandong University School of Information Science and Engineering Qingdao266237 China Air Force Medical University Department of Military Medical Psychology Xi'an710032 China

ISBN: (纸本)9798350330755

Remote photoplethysmography (rPPG) can use optical sensors such as cameras to capture the periodicity of skin color caused by heartbeats, and further calculate physiological signals by algorithms. In the future, rPPG technology will be widely applied on the mobile terminal, which has huge potential value. This paper designs an end-to-end heart rate signal detection network PhysMViT based on multi-scale Transformer, and builds a pyramid structure in the transformer to mine different scale information of facial video sequence feature maps. Subsequently, the designed network PhysMViT was trained on two public datasets UBFC and MMPD, and the training effect was verified. The experimental results show that the PhysMViT network has achieved good accuracy in all indicators on the dataset UBFC, and the indicators on the MMPD have also exceeded most networks. Finally, ablation experiments are carried out to verify the effectiveness of the design of each part of the PhysMViT network. © 2023 IEEE.

关键词： Deep learning

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Research on Digital Watermarking Algorithm Based on Discrete Cosine Transform 15

Research on Digital Watermarking Algorithm Based on Discrete...

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15th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2022

作者： Duang, Yifang Wang, Yi Cao, Congjun Zhang, Xiaolin XI'an University of Technology Faculty of Printing Engineering and Digital Media Technology Xi'an China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Digital image watermark technology plays a very critical role in the fields of copyright maintenance and information dissemination. This paper proposes discrete cosine transform digital watermark algorithm based on the frequency domain digital watermark image of watermark algorithm and human visual features of digital watermark algorithm. The watermark image loading, pretreatment, embedding, extraction, and attack of the dynamic watermark test is realized, and the block embedded watermark information is reached. The algorithm is to combine the DCT block method and the experiment result shows good robustness features, contribute to copyright information protection. © 2022 IEEE.

关键词： Discrete cosine transforms

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An Automatic Orientation Method of Tibial Sagittal Plane Based on Anatomical and Statistical Features 16

An Automatic Orientation Method of Tibial Sagittal Plane Bas...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Chen, Hanyu Pei, Yuhan Xiao, Liying Xiong, Ran Hao, Zhixiu Tsinghua University Department of Mechnical Engineering Beijing100084 China Third Military Medical University Center for Joint Surgery Southwest Hospital Chongqing400036 China

ISBN: (纸本)9798350330755

Preoperative planning of total knee arthroplasty (TKA) has a significant impact on the quality of the surgery. Nowadays, determining the orientation of the tibia and femur during preoperative planning usually relies on manual implementation by experienced operators, which is complex and lacks of agreement among observers. In this paper, we propose an automatic tibial sagittal plane orientation method to determine the tibial sagittal plane. Based on the statistical information, the position of the axial plane where the tibial tuberosity is located is defined. The lateral border of the tibial tuberosity (lTT) is determined as the farthest point away from the projection point of the mechanical axis in the axial plane. Then the angle between the lTT and the medial third of the tibial tuberosity (m3TT) is calculated according to the anatomical characteristics of the tibial contour. Tibial sagittal plane is determined as the plane which passes through the mechanical axis and the m3TT. For the given tibial model, the result is unique and has the highest repeatability in contrast to other alignment approaches involving manual operations. The average error of our method is -1.9° ± 11.6° compared to the observer's result. This method reduces the cost of manual operation in the preoperative planning process of TKA, whereas the repeatability eliminates the measurement error between multiple operations, and provides an effective reference for preoperative planning. © 2023 IEEE.

关键词： Bone

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