生物医学工程学杂志最新文献_第8页

[Fetal electrocardiogram signal extraction based on multi-scale residual shrinkage U-Net]. [基于多尺度残差收缩 U-Net 的胎儿心电图信号提取]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202303012

Qian Wang, Zhengxu Zhang, Danyang Song, Yujing Wang, Lixin Song

{"title":"[Fetal electrocardiogram signal extraction based on multi-scale residual shrinkage U-Net].","authors":"Qian Wang, Zhengxu Zhang, Danyang Song, Yujing Wang, Lixin Song","doi":"10.7507/1001-5515.202303012","DOIUrl":"10.7507/1001-5515.202303012","url":null,"abstract":"In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"494-502"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Multi-scale medical image segmentation based on pixel encoding and spatial attention mechanism]. [基于像素编码和空间注意力机制的多尺度医学图像分割]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202310001

Yulong Wan, Dongming Zhou, Changcheng Wang, Yisong Liu, Chongbin Bai

{"title":"[Multi-scale medical image segmentation based on pixel encoding and spatial attention mechanism].","authors":"Yulong Wan, Dongming Zhou, Changcheng Wang, Yisong Liu, Chongbin Bai","doi":"10.7507/1001-5515.202310001","DOIUrl":"10.7507/1001-5515.202310001","url":null,"abstract":"In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"511-519"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion]. [基于结构磁共振成像与双时间点融合的早期阿尔茨海默病辅助诊断集合模型]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202310046

An Zeng, Jianbin Wang, Dan Pan, Yang Yang, Jun Liu, Xin Liu, Wenge Chen, Juhua Wu

{"title":"[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].","authors":"An Zeng, Jianbin Wang, Dan Pan, Yang Yang, Jun Liu, Xin Liu, Wenge Chen, Juhua Wu","doi":"10.7507/1001-5515.202310046","DOIUrl":"10.7507/1001-5515.202310046","url":null,"abstract":"Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"485-493"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging]. [基于多模态磁共振成像深度可分离卷积的缺血性中风梗塞分割模型]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202308001

Yidong Jin, Mengfei Wang, Jingjing Chen, Yuehua Li

{"title":"[Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging].","authors":"Yidong Jin, Mengfei Wang, Jingjing Chen, Yuehua Li","doi":"10.7507/1001-5515.202308001","DOIUrl":"10.7507/1001-5515.202308001","url":null,"abstract":"Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"535-543"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Precise measurement of human heart rate based on multi-channel radar data fusion]. [基于多通道雷达数据融合的人体心率精确测量]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202307010

Hongrui Guo, Huimin Cao, Keqi Yang, Zhushanying Zhang

{"title":"[Precise measurement of human heart rate based on multi-channel radar data fusion].","authors":"Hongrui Guo, Huimin Cao, Keqi Yang, Zhushanying Zhang","doi":"10.7507/1001-5515.202307010","DOIUrl":"10.7507/1001-5515.202307010","url":null,"abstract":"To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [-4.78, 4.78] beats per minute, and a consistency error of -0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"461-468"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research progress on the mechanisms of probiotics promoting wound healing]. [益生菌促进伤口愈合机制的研究进展]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202208003

Jiali Dong, Xuejing Wang, Geyan Bai, Dong Wang

引用次数: 0

[Review of joint attention deficit intervention based on virtual reality for children with autism]. [基于虚拟现实技术的自闭症儿童联合注意力缺陷干预综述]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202305029

Xin Zhao, Heting Wang, Xinmeng Guo, Ludan Zhang, Wei Liu, Shuang Liu, Dong Ming

引用次数: 0

[Study on the mesoscopic dynamic effects of tumor treating fields on cell tubulin]. [肿瘤治疗场对细胞微管蛋白的介观动态效应研究]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202312063

Xing Li, Kaida Liu, Cong Guo, Tianrui Fang, Fan Yang

{"title":"[Study on the mesoscopic dynamic effects of tumor treating fields on cell tubulin].","authors":"Xing Li, Kaida Liu, Cong Guo, Tianrui Fang, Fan Yang","doi":"10.7507/1001-5515.202312063","DOIUrl":"10.7507/1001-5515.202312063","url":null,"abstract":"Tumor treatment fields (TTFields) can effectively inhibit the proliferation of tumor cells, but its mechanism remains exclusive. The destruction of cellular microtubule structure caused by TTFields through electric field force is considered to be the main reason for inhibiting tumor cell proliferation. However, the validity of this hypothesis still lacks exploration at the mesoscopic level. Therefore, in this study, we built force models for tubulins subjected to TTFields, based on the physical and electrical properties of tubulin molecules. We theoretically analyzed and simulated the dynamic effects of electric field force and torque on tubulin monomer polymerization, as well as the alignment and orientation of α/β tubulin heterodimer, respectively. Research results indicate that the interference of electric field force induced by TTFields on tubulin monomer is notably weaker than the inherent electrostatic binding force among tubulin monomers. Additionally, the electric field torque generated by the TTFileds on α/β tubulin dimers is also difficult to affect their random alignment. Therefore, at the mesoscale, our study affirms that TTFields are improbable to destabilize cellular microtubule structures via electric field dynamics effects. These results challenge the traditional view that TTFields destroy the microtubule structure of cells through TTFields electric field force, and proposes a new approach that should pay more attention to the \"non-mechanical\" effects of TTFields in the study of TTFields mechanism. This study can provide reliable theoretical basis and inspire new research directions for revealing the mesoscopic bioelectrical mechanism of TTFields.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"569-576"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[In vivo tumor imaging and therapy based on near-infrared cadmium-free quantum dots]. [基于近红外无镉量子点的体内肿瘤成像和治疗]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202404002

Xiaoqi Li, Xiao Sun, Hua Lin, Peisen Zhang, Mingxia Jiao, Ni Zhang

{"title":"[In vivo tumor imaging and therapy based on near-infrared cadmium-free quantum dots].","authors":"Xiaoqi Li, Xiao Sun, Hua Lin, Peisen Zhang, Mingxia Jiao, Ni Zhang","doi":"10.7507/1001-5515.202404002","DOIUrl":"10.7507/1001-5515.202404002","url":null,"abstract":"Near-infrared fluorescence imaging technology, which possesses superior advantages including real-time and fast imaging, high spatial and temporal resolution, and deep tissue penetration, shows great potential for tumor imaging in vivo and therapy. Ⅰ-Ⅲ-Ⅵ quantum dots exhibit high brightness, broad excitation, easily tunable emission wavelength and superior stability, and do not contain highly toxic heavy metal elements such as cadmium or lead. These advantages make Ⅰ-Ⅲ-Ⅵ quantum dots attract widespread attention in biomedical field. This review summarizes the recent advances in the controlled synthesis of Ⅰ-Ⅲ-Ⅵ quantum dots and their applications in tumor imaging in vivo and therapy. Firstly, the organic-phase and aqueous-phase synthesis of Ⅰ-Ⅲ-Ⅵ quantum dots as well as the strategies for regulating the near-infrared photoluminescence are briefly introduced; secondly, representative biomedical applications of near-infrared-emitting cadmium-free quantum dots including early diagnosis of tumor, lymphatic imaging, drug delivery, photothermal and photodynamic therapy are emphatically discussed; lastly, perspectives on the future directions of developing quantum dots for biomedical application and the faced challenges are discussed. This paper may provide guidance and reference for further research and clinical translation of cadmium-free quantum dots in tumor diagnosis and treatment.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"620-626"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A medical visual question answering approach based on co-attention networks]. [基于共注意力网络的医学视觉问题解答方法]。

生物医学工程学杂志 Pub Date : 2024-06-25 DOI: 10.7507/1001-5515.202307057

Wencheng Cui, Wentao Shi, Hong Shao

{"title":"[A medical visual question answering approach based on co-attention networks].","authors":"Wencheng Cui, Wentao Shi, Hong Shao","doi":"10.7507/1001-5515.202307057","DOIUrl":"10.7507/1001-5515.202307057","url":null,"abstract":"Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of \"visual attention\" crucial, but the modeling of \"question attention\" is equally significant. To facilitate bidirectional reasoning in the attention processes involving medical images and questions, a new MVQA architecture, named MCAN, has been proposed. This architecture incorporated a cross-modal co-attention network, FCAF, which identifies key words in questions and principal parts in images. Through a meta-learning channel attention module (MLCA), weights were adaptively assigned to each word and region, reflecting the model's focus on specific words and regions during reasoning. Additionally, this study specially designed and developed a medical domain-specific word embedding model, Med-GloVe, to further enhance the model's accuracy and practical value. Experimental results indicated that MCAN proposed in this study improved the accuracy by 7.7% on free-form questions in the Path-VQA dataset, and by 4.4% on closed-form questions in the VQA-RAD dataset, which effectively improves the accuracy of the medical vision question answer.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 3","pages":"560-568"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0